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Upwork Top Rated Plus  路  AWS 路 Azure 路 GCP Certified

Your AI prototype works in the demo. It falls apart in production. That is the part we build.

We design and ship RAG systems, AI agents, and LLM SaaS products that hold up when real data, real users, and real compliance requirements hit them. Documented architecture, working backend, cloud deployment, and a clean handoff. Every time.

Book a Free 30-Min Call See the Work
22 systems in production
100% job success
27 enterprise clients
production_pipeline.yaml
Document Ingestionhybrid retrieval 路 reranking
91%accuracy
Agent Orchestrationlanggraph 路 approval gates
24/7uptime
Observability and Driftlangsmith 路 evidently
livemonitoring
AWS Certified
Azure Certified
GCP Certified
Upwork Top Rated Plus
100% Job Success
USA 路 UK 路 EU 路 Gulf
Building on the latest 2026 models OpenAI GPT-5.5Claude Opus 4.8Gemini 3 ProLlama 4Mistral Large 3DeepSeek V3Qwen 3
22+Production AI Systems
27+Enterprise Clients
100%Job Success Rate
47%Avg Efficiency Gain
$870KClient Costs Saved
24/7Production Support
Teams that have shipped to production with us
TechCorp SolutionsHealthTech InnovationsEFS NetworksNexaScale SystemsEuroCore GroupPropIntelLegalEdge UK
Core Expertise

Six things we do at a level most teams cannot match

We are not generalists or prompt writers. We are AI engineers who have put production systems in front of real users across six technical practice areas.

RAG systems that actually retrieve

Most RAG projects fail at the retrieval layer, not the model. We build with hybrid search (BM25 plus dense vectors), cross-encoder reranking, citation grounding, and RAGAS evaluation against ground-truth benchmarks before anything goes live. Your knowledge base becomes searchable in seconds, with sources attached.

LangChainLlamaIndexRAG FusionPineconeWeaviate

AI agents that complete real tasks

Not chatbots. Agents that connect to your APIs, databases, and tools, then take action and report back. We build on LangGraph and CrewAI with human approval gates, retry logic, and full trace observability, so you always know what the agent did and why it did it.

LangGraphCrewAIn8nAutoGen

Workflow automation that runs around the clock

We self-host n8n inside your own infrastructure, so your data never leaves the building. One platform, 400 plus integrations, AI decision logic baked into every workflow. No per-operation pricing, no vendor lock-in, no surprise downtime.

n8nMake.comPower AutomateZapier

LLM products with a real backend

Full AI products from scratch. Backend APIs, authentication, multi-tenancy, usage tracking, dashboards, database design, and cloud deployment, all in one engagement. You ship a product in weeks, not quarters, and you own the whole stack at the end.

FastAPIReactPostgreSQLDockerStreamlit

MLOps built for regulated environments

Full-lifecycle ML infrastructure: training pipelines, model registries, shadow deployment, statistical drift detection, A/B testing, and CI/CD. Certified on AWS SageMaker, Azure ML, and GCP Vertex AI, with audit-ready lineage from the first commit.

MLflowZenMLLangSmithKubernetesEvidently

Fine-tuning with ground-truth validation

We fine-tune domain-specific models on your proprietary data using QLoRA and DPO alignment. Every model is validated against held-out benchmarks with ROUGE, BERTScore, and citation accuracy before it touches a single production request.

QLoRAPEFTDPORLHFGemmaLLaMA
Services

Everything you need to ship AI, under one roof

From scoping through deployment and support. One partner, full accountability, no vendor juggling.

RAG and Document Intelligence

Search, understand, and answer from PDFs, databases, CRMs, contracts, support tickets, and internal wikis. Hybrid retrieval, reranking, citation grounding, access control, and a production evaluation pipeline come standard.

Hybrid SearchRerankingCitationsRBAC

AI Agents and Task Automation

Agents that draft emails, review documents, qualify leads, run compliance checks, and handle internal operations, connected directly to your CRM, databases, and APIs. Full audit trails and human approval where the stakes are high.

LangGraphCrewAIn8nAutoGen

LLM SaaS and Internal Copilots

We build the whole product. Backend APIs, user authentication, admin dashboards, usage analytics, database schema, and cloud deployment. Copilots for support, sales, HR, legal, and operations teams that your staff will actually use.

FastAPIReactPostgreSQLCloud

Machine Learning Systems

Forecasting, fraud detection, recommendation engines, and predictive analytics, with evaluation frameworks, drift monitoring, and retraining pipelines built in. Systems that get better over time instead of quietly going stale.

PyTorchXGBoostMLflowAWS Bedrock

Cloud AI and MLOps Infrastructure

Production-ready ML infrastructure on AWS, Azure, and GCP. Automated CI/CD, model registries, shadow deployment, drift alerts, and cost optimization. Triple-certified, with zero-downtime deployments as the baseline.

SageMakerAzure MLVertex AIKubernetes

AI Strategy and Architecture Review

Not sure whether to build or buy? Which model fits your use case? We map your workflow, design the architecture, model the ROI, and hand you a phased plan you can act on. No fluff, just a technical plan that survives contact with reality.

ArchitectureROI ModelingBuild vs Buy

Intelligent Process Automation

Combines RPA, AI, and NLP to automate the messy workflows that rule-based bots break on. Document processing, compliance checks, data extraction, and multi-system orchestration, all without disrupting your live operations.

UiPathBlue PrismDocAINLP

LLM Fine-Tuning and Alignment

Domain-specific fine-tuning on your proprietary data using QLoRA, LoRA, and DPO alignment. Production evaluation pipelines with ROUGE, BERTScore, and human preference scoring. Every model is validated before deployment, not after.

QLoRADPOPEFTRLHF

Data Engineering and Analytics

Data pipelines, warehouse integrations, and BI dashboards that feed your AI systems clean, structured data. Snowflake, Databricks, BigQuery, and real-time streaming with Kafka and Airflow, built to scale with you.

SnowflakeDatabricksPower BIAirflow
Where to Start

Which of these sounds like you?

Most engagements start with one of these problems. Pick the one that fits and we will tell you what solving it looks like.

"We have documents no one can search"

Contracts, wikis, tickets, and PDFs full of answers nobody can find fast. A RAG system makes all of it searchable in seconds, with citations and access control.

Talk to us about RAG

"My team drowns in manual work"

The same repetitive steps eating hours every week. AI agents and self-hosted automation take the work off your team and run it around the clock.

Talk to us about agents

"We need our own AI tool, not another subscription"

A copilot or LLM product built around your data and your workflow, with the backend, dashboards, and deployment included. You own the whole thing.

Talk to us about LLM SaaS

"Our AI works in testing but breaks in production"

A prototype that falls apart under real data and real load. We harden it with evaluation, monitoring, drift detection, and a proper MLOps pipeline.

Talk to us about hardening
Intelligent Process Automation

Automation that reasons, not just clicks

Standard RPA follows fixed rules. Our IPA layer adds AI reasoning, NLP, and computer vision to handle the variable, judgment-heavy work that conventional bots always break on.

End-to-End Process Automation

We map your highest-cost manual workflows, find the automation opportunities that matter, and build systems that remove the work entirely. Not faster humans. No human needed for these tasks at all.

IPA Managed Services

We run your automation infrastructure for you. Round-the-clock monitoring, proactive health checks, incident response inside two hours, and monthly optimization reviews. Your bots keep running while your team stays focused.

Team Augmentation

Need AI engineers inside your team? We embed automation specialists, RPA developers, and AI architects directly into your delivery workflow. Enterprise-grade talent, no hiring cycle, no overhead.

Custom IPA Solutions

Built around your exact workflow constraints, tech stack, and compliance requirements. Includes full system integration, team training, documentation, and a 90-day transition roadmap so your team owns it cleanly.

How We Work

A delivery process built for production, not demos

Every engagement follows the same disciplined path. You see real output early, and you always know where the project stands.

1

Scope and KPIs

A free call to understand the workflow, the data, and the constraints. We define what success looks like in measurable terms before anyone writes code.

// week 0
2

Architecture and Plan

A documented system design, model selection, cost model, and phased delivery plan. You approve the approach and the budget before we start building.

// week 1
3

Build and Demo

Agile sprints with a working demo from sprint two onward. Weekly updates, honest reporting on blockers, and evaluation against the KPIs we set.

// weeks 2 to N
4

Deploy and Hand Off

Cloud deployment, monitoring, full documentation, and a clean handoff. Then we stay on for support, model tuning, and quarterly improvements.

// launch and beyond
Case Studies

Ten production systems, real enterprise clients

Every number below comes from a live system in production. No projections, no demos, no invented metrics.

LegalTechUSA and UKQLoRA 路 DPO 路 GCP Vertex AI

Domain-specific legal model that stopped hallucinating citations

The Problem

A major international law firm was running general-purpose models that hallucinated citations and produced inconsistent clause analysis across 11 jurisdictions. Every wrong answer was a liability risk in client-facing work.

What We Built

We fine-tuned Gemma 3 27B on 400,000 of the firm's own legal documents using QLoRA on GCP Vertex AI. DPO alignment sharpened precision for time-pressured lawyers. A custom evaluation pipeline covering ROUGE-L, BERTScore, and citation accuracy ran before any model reached production.

400K Legal Docs Cases 路 Contracts 11 Jurisdictions QLoRA Fine-Tuning GCP Vertex AI DPO Alignment PEFT 路 Curation Hugging Face TRL Gemma 3 27B Legal Expert Model LangSmith Eval Pinecone 路 FastAPI 91% Accuracy Citation Precision Zero Hallucinations 60% Lower Cost
91%Citation accuracy on legal queries
0Hallucinated citations after alignment
27BParameter model on 400K proprietary docs
60%Lower inference cost vs proprietary APIs
"What sets Jillani SofTech apart is the ability to turn complex regulatory requirements into a system that actually runs. Most vendors talk about compliance. These engineers build for it."
Lisa Thompson, Chief Compliance Officer, Global Enterprises, UK
Financial ServicesUSAMLflow 路 AWS SageMaker 路 SOC 2

One governance layer for five production models in a regulated FinTech

The Problem

Five AI models were running in production with no shared visibility, no drift detection, and no governance. Model behavior was tracked by hand in spreadsheets. In a regulated financial environment, that is an audit failure waiting to happen.

What We Built

A single LLMOps control plane across all five models: scheduled automated evaluations, statistical drift detection against rolling baselines, shadow deployment and A/B testing before any version reaches live traffic, and a full model registry with SOC 2 compliant lineage and approval workflows.

Risk ScoringModel 1 Fraud DetectionModel 2 AML + AdvisoryModels 3-5 Governance Control Plane MLflow 路 ZenML 路 Evidently Shadow Deploy 路 A/B AWS SageMaker 路 LangSmith Unified Dashboard SOC 2 Audit Logs Prometheus 路 Grafana Real-time Alerts Auto Promotion Data-driven rollout Safe rollback
5Production models unified under one platform
AutoDrift detection replacing manual review
SOC 2Audit-ready model lineage for compliance
3xFaster safe promotion via shadow testing
"The systems are solid, the documentation is thorough, and the team stayed accountable well past the delivery date. That mix of technical depth and post-launch ownership is rare at this level."
Evan Solomon, CEO, EFS Networks, USA
Enterprise SaaSUSA and UKn8n 路 LangGraph 路 GPT-5.5

An autonomous revenue layer that closed the gaps no rep could watch

The Problem

Fourteen disconnected sales tools, no consistent lead qualification, and shaky CRM data. Revenue was slipping through gaps that no single person could track across multiple regions at once.

What We Built

A round-the-clock revenue layer on n8n and LangGraph. Real-time lead qualification, automatic CRM enrichment, personalized outreach across email and LinkedIn, predictive deal health scoring, and live pipeline reports delivered to leadership with no sales ops in the loop.

Lead Sources Inbound 路 Outbound HubSpot 路 Salesforce 900+ triggers/day AI Agent Orchestration n8n self-hosted 路 LangGraph GPT-5.5 路 ML Lead Scoring Outreach Sequencing Stripe 路 Slack 路 Gmail API Human approval gates Pipeline Intelligence Deal health scoring Live leadership reports Zero manual ops $340K Pipeline in 6 months 48% less manual work
48%Reduction in manual sales ops workload
1.8xIncrease in qualified lead throughput
$340KPipeline generated in first 6 months
900+Automated workflow triggers daily
"Their revenue platform gave us the pipeline intelligence we had spent two years trying to build internally. The results were immediate and measurable."
Michael Stevens, CTO, TechCorp Solutions, USA
HealthcareUSAAgentic RAG 路 GCP Vertex AI 路 HIPAA

HIPAA-compliant clinical decision support for a hospital network

The Problem

A US hospital network had patient records, lab results, and ICD mappings sitting in silos, none of it reachable at the point of care. Clinicians needed decision support without adding friction to already demanding workflows.

What We Built

A HIPAA-compliant clinical platform on GCP Vertex AI using a hybrid Agentic RAG pipeline. Patient records, lab data, and medical literature unified into one queryable layer. Role-based access across every clinical and admin role, a full audit trail on every response, and a zero-downtime SLA.

Patient RecordsEHR 路 FHIR Lab ResultsICD-10 路 BigQuery Medical LiteratureWeaviate 路 RAGatouille Agentic RAG Pipeline LangGraph 路 Claude Opus 4.8 RBAC 路 Audit Log GCP Vertex AI 路 Docker Clinical Decision AI HIPAA Compliant Zero-downtime SLA Plain-language queries 91% Factual accuracy 48% less manual docs
91%Factual accuracy in clinical responses
27%Reduction in patient onboarding time
48%Less manual clinical documentation
HIPAACompliance built into the architecture
"The mix of factual accuracy, HIPAA-compliant architecture, and real-time performance had a direct, measurable effect on both patient care quality and throughput across our network."
Dr. Rachel Chen, Chief Medical Officer, HealthTech Innovations, USA
Enterprise SaaSUSA and UKLangGraph 路 RAG Fusion 路 Neo4j

Knowledge search that handles multi-document reasoning at scale

The Problem

Critical institutional knowledge was scattered across hundreds of documents and disconnected systems. A single-vector-search setup had already failed. The organization needed multi-document, multi-hop reasoning that held up under real query volume.

What We Built

Multi-agent reasoning with a Neo4j knowledge graph for complex cross-document queries. RAG Fusion lifted precision well above single-retrieval approaches. Role-based access with a complete audit trail across every department, handling 8,000 queries per day at launch.

Knowledge Sources PDFs 路 Wikis 路 CRM ERPs 路 Slack 路 Email AWS S3 路 Databases 8K+ queries/day Retrieval Layer RAG Fusion 路 Pinecone Neo4j Knowledge Graph Cross-encoder Reranking LangGraph Multi-agent RBAC 路 Full Audit Trail Cited Answers GPT-5.5 路 Azure OpenAI Source citations Streamlit UI Plain language 54% Faster search 61% better precision 37% fewer tickets
54%Faster knowledge retrieval
61%Better retrieval precision vs prior tooling
37%Fewer support tickets from knowledge gaps
8K+Daily queries across departments
"They think about AI the way a senior technology architect thinks about enterprise systems. Not tools to bolt on, but infrastructure to build around."
Frank Shines, Head of AI and Digital Transformation, USA
E-CommerceUSA and UKn8n 路 Claude Sonnet 路 GPT-5.5

Support that handled growing volume with flat headcount

The Problem

A fast-scaling US e-commerce brand had support infrastructure buckling under volume. Response times were slipping, agents were overwhelmed, and brand content across five social platforms was inconsistent. Hiring more people was not the fix.

What We Built

An autonomous support platform across Instagram, TikTok, Facebook, LinkedIn, X, chat, and email. Claude Sonnet handles incoming queries with knowledge-grounded reasoning, processing refunds, routing tickets, and resolving most issues without escalation. A sentiment layer escalates only what needs human judgment.

Customer Channels Instagram 路 TikTok Facebook 路 LinkedIn X 路 Chat 路 Email 24/7 inbound Graph APIs 路 Webhooks AI Support Brain n8n 路 Claude Sonnet 4.6 GPT-5.5 路 Pinecone RAG Sentiment Monitoring LangChain 路 PostgreSQL AWS Lambda 路 Docker Resolution Layer Auto-resolve or escalate Refunds 路 Routing 路 FAQs Brand tone enforced 61% Resolved without humans 44% faster response
61%Queries resolved without escalation
44%Reduction in average response time
23%Improvement in audience engagement
24/7Global coverage, zero added headcount
"Customer volume grew substantially after launch while headcount stayed flat. The AI handles what would have taken three full-time agents. It changed our support economics."
VP of Customer Experience, US E-Commerce Brand
RegTechGermany and EUAgentic RAG 路 Azure OpenAI 路 LLMOps

Regulatory intelligence across eight countries and three languages

The Problem

A major European enterprise was managing GDPR, EU CSRD sustainability mandates, and internal policy review across eight countries at once. Each compliance cycle pulled in outside legal consultants and burned months of manual effort.

What We Built

A platform that monitors regulatory feeds across every relevant framework, analyzes internal documents for compliance gaps in real time, raises risk flags with structured remediation steps, and produces board-ready reports in English, German, and French on demand. An LLMOps governance layer keeps every decision auditable.

Regulatory Feeds GDPR 路 CSRD 路 AI Act Internal Policies 8 Country Feeds Azure Blob Storage Compliance AI Engine LangChain 路 GPT-5.5 Azure ChromaDB 路 FastAPI MLflow 路 LangSmith Streamlit Dashboard Docker 路 CI/CD Board-Ready Reports EN 路 DE 路 FR Risk flags + remediation Auditable decisions Pinecone 路 Grafana 84% Gap classification 52% less audit work 2.3x faster reporting
84%Accuracy in automated gap classification
52%Reduction in manual ESG auditing per cycle
2.3xFaster reporting across all jurisdictions
3Languages: English, German, French
"Their platform reduced our risk exposure and improved audit readiness across multiple jurisdictions. Most vendors talk about compliance. These engineers build for it."
Chief Compliance Officer, Global Enterprise, UK
DevOps and EngineeringUSALangGraph 路 AutoGen 路 GPT-5.5

An engineering org that shipped faster by spending less time fixing the pipeline

The Problem

An engineering organization was spending more capacity managing its delivery pipeline than shipping product. Debugging was reactive, the same failure patterns kept returning across sprints, and incident postmortems were inconsistent when they happened at all.

What We Built

A multi-agent platform that plugs into the existing DevOps toolchain. It reviews pull requests before merge, diagnoses pipeline failures with specific fixes, generates validated patches and test cases, watches deployments for anomalies, and writes structured incident summaries after every significant event.

DevOps Signals GitHub 路 Jenkins Pull Requests Pipeline Failures AWS CloudWatch Multi-Agent CI/CD Brain GPT-5.5 路 LangGraph AutoGen 路 LangChain Code review 路 Test gen LangSmith 路 MLflow PgVector 路 FastAPI Automated Actions PR comments + fixes Incident summaries Deploy anomaly alerts Kubernetes 路 Docker 38% Less debug time 29% faster deploys Fewer regressions
38%Less debugging and incident resolution time
29%Faster deployment cycles across environments
FewerProduction regressions per sprint
LowerManual DevOps work per delivery cycle
"The autonomous delivery platform changed how our engineering org operates. Any team focused on sustained velocity without sacrificing quality should be talking to this team."
Daniel Foster, Director of Engineering, NexaScale Systems, USA
Global EnterpriseUSA and EuropeGPT-5.5 路 LangGraph 路 Neo4j

Program governance that flags risk before it escalates

The Problem

A global enterprise running complex programs across multiple regions had no real-time view of execution. Status updates were manual summaries from people with a stake in how they read. Risks surfaced only after they had escalated, and dependencies lived in spreadsheets that were stale before leadership saw them.

What We Built

A program management layer that ingests live communication from Slack, email, and ticketing. It tracks timelines and blockers as they develop, raises predictive risk flags before they escalate, and produces clean executive briefings on demand. A persistent decision-memory layer preserves context across leadership changes.

Live Data Feeds Slack 路 Email Jira 路 Tickets Multi-region programs Cross-team dependencies AI Delivery Intelligence GPT-5.5 路 LangGraph Neo4j Knowledge Graph Pinecone 路 Azure OpenAI LangSmith 路 FastAPI Decision memory layer Executive Intelligence Predictive risk flags On-demand briefings Live blocker tracking Docker 路 PostgreSQL 28% Better on-time delivery Earlier risk detection Less manual reporting
28%Improvement in on-time delivery
EarlierRisk identification across teams
ReducedManual overhead in status reporting
LiveExecutive visibility across pipelines
"The platform gave our executive team real-time visibility into risks, dependencies, and execution gaps before they became problems. It works more like an intelligent operations layer than a reporting tool."
Isabella Muller, VP Strategy and Operations, EuroCore Group, Germany
Retail and E-CommerceUSAAWS Bedrock 路 RLHF 路 Snowflake

Two compounding problems solved in one platform: conversion and inventory

The Problem

Conversion was flat because the experience was identical for every segment. Inventory costs kept climbing because demand planning was reactive and manual. Two problems feeding each other, neither one solved by the existing tools.

What We Built

A dual-layer platform. The personalization layer generates real-time recommendations from live behavioral signals and improves through reinforcement learning. The supply chain layer predicts demand shifts and adjusts inventory planning before overstock or stockout hits. Both run through AWS Bedrock under 100ms.

Customer Layer Behavior signals Session data 路 Clicks Supply Chain Layer Inventory 路 Sales history Snowflake 路 Airflow Dual-Layer AI Platform AWS Bedrock 路 RLHF LangChain 路 Scikit-learn PostgreSQL 路 pgvector MLflow 路 FastAPI Sub-100ms response Personalization Engine Real-time recommendations 14% conversion uplift Demand Forecasting 62% better accuracy 31% inventory improvement 14% Conversion increase 62% Better forecasting
14%Increase in e-commerce conversion rate
31%Improvement in inventory planning accuracy
62%Improvement in demand forecasting accuracy
100msSub-100ms recommendation response
"Two problems we had fought for three years, solved in one platform. The personalization numbers spoke for themselves inside the first 30 days, and inventory planning changed how our buying team works."
VP of Digital, Enterprise Retail Group, USA
engineering_stack
LLM Orchestrationlangchain 路 langgraph 路 crewai
prod
Vector and Knowledge Layerpinecone 路 weaviate 路 neo4j
active
Automation Enginen8n 路 crewai 路 power automate
active
Cloud Infrastructuresagemaker 路 azure ml 路 vertex
certified
Observabilitymlflow 路 langsmith 路 grafana
24/7
How We Work

No demos. No notebooks. Only working systems.

One partner, full accountability

Architecture through deployment through support. No vendor coordination, no accountability gaps. You have one person to call.

Production-ready from the first sprint

Every system is tested against defined success criteria before it goes live. Clean architecture, documented handoff, real monitoring from day one.

KPIs before code

We define what success looks like before writing a line. Efficiency gains, cost reductions, retrieval accuracy. We track them throughout.

You see progress every week

Working demos from sprint two, structured updates, and honest communication about blockers. No surprises at handoff.

Outcomes

Numbers from live production systems

Not projections or theoretical benchmarks. These come from deployed systems with real clients.

85%
Average Process Efficiency Gain
Across all automation deployments
65%
Reduction in Manual Workload
Via AI agents and workflow automation
91%
Best-in-class RAG Accuracy
Healthcare and legal systems
$870K
Client Costs Saved and Documented
Across all engagements
100%
Job Success Rate on Upwork
Every client satisfied, every project delivered
3-6mo
Typical ROI Payback Period
Most enterprise engagements

Not sure AI is worth it for your workflow yet?

Start with a free 30-minute AI audit. We look at one real workflow, estimate the ROI, and tell you honestly whether to build, buy, or wait. No pitch, no obligation.

Get a Free AI Audit
Engagement Models

Clear scope, clear pricing, no surprises

Pick the path that fits where you are. Every engagement ends with a working system and a clean handoff. Final pricing is confirmed after a free scoping call.

RAG or Automation Build
A focused system that solves one clear problem, shipped fast.
from $3,500
// 2 to 4 weeks
  • RAG chatbot or n8n workflow on your data
  • Hybrid retrieval, reranking and citations
  • Cloud deployment and documentation
  • Evaluation against ground-truth benchmarks
  • 30-day post-launch support
Scope This Build
Enterprise AI Platform
A full product with agents, APIs, dashboards and governance.
$15K to $60K+
// 8 to 16 weeks
  • Multi-agent systems and LLM SaaS
  • Backend, auth, multi-tenancy and analytics
  • Full MLOps and LLMOps pipeline
  • HIPAA, GDPR or SOC 2 architecture
  • Monitoring, SLAs and quarterly reviews
Plan Your Platform
Retainer and Team Augmentation
Ongoing engineering capacity embedded in your team.
Monthly
// rolling engagement
  • Dedicated AI engineering hours each month
  • Model tuning and retrieval improvements
  • Incident response and managed monitoring
  • Architecture reviews and roadmap input
  • Priority access to the engineer directly
Discuss a Retainer
Engagement Guarantees

What every engagement gives you, in writing

Hiring an outside team is a risk. We remove as much of it as we can before you ever sign.

You own everything

Full source code, documentation, and complete IP transfer on delivery. Everything runs in your own cloud accounts and repositories. No black boxes, nothing licensed back to us.

A working demo by sprint two

You see a real, running system inside the first two weeks, not slideware. Every sprint ships something you can click, test, and judge for yourself.

Fixed scope, clear quote

We agree the scope, deliverables, and price before any code is written. No open-ended hourly surprises, no scope creep quietly billed back to you.

Security and NDA from day one

NDA on request, least-privilege access, and HIPAA, GDPR, or SOC 2 controls built into the architecture. We never train external models on your data.

No vendor lock-in

Built on open standards inside your infrastructure. You can run, extend, or hand the system to another team at any time without depending on us.

We stay after launch

Monitoring, incident response, model tuning, and quarterly reviews are part of every engagement. We do not build it and disappear.

Industries

We know your sector's constraints

Every industry has its own regulations, data architecture, and operational reality. We build for those specifics, not generic templates.

Banking and Finance

Fraud detection, credit risk scoring, compliance automation, AML monitoring, and AI advisory platforms built for FSA, SEC, MiFID II, and Basel III, with full auditability.

Healthcare

HIPAA-compliant clinical AI, medical documentation automation, diagnostic support, and hospital operations systems running under zero-downtime SLAs.

Legal and Compliance

Contract analysis, regulatory monitoring, e-discovery acceleration, and GDPR plus CSRD compliance systems for law firms and in-house legal teams.

Retail and E-Commerce

Demand forecasting, personalized recommendations, dynamic pricing, autonomous customer support, and inventory intelligence at scale.

SaaS and Technology

Internal AI copilots, developer productivity tools, customer success AI, multi-tenant LLM SaaS products, and AI-powered feature development.

Manufacturing and Supply Chain

Predictive maintenance AI, computer vision quality control, supply chain optimization, and production scheduling automation.

Real Estate

Property valuation, market forecasting, lead qualification, document automation, tenant support, and portfolio insights for residential and commercial teams.

Human Resources

Resume screening, candidate matching, hiring workflows, workforce analytics, engagement tracking, and attrition prediction for growing HR teams.

Client Feedback

What clients say after we deliver

Direct quotes from the people we built for. Every testimonial is tied to a real delivery.

Their revenue platform gave us the pipeline intelligence we had spent two years trying to build internally. The results were immediate and measurable. I would recommend them for any serious enterprise AI initiative.

MS
Michael StevensCTO, TechCorp Solutions, USA
Verified

The clinical platform runs at a level we expect from top-tier enterprise vendors. Factual accuracy, HIPAA compliance, and real-time performance. It had a direct, measurable effect on patient care quality and throughput across our network.

RC
Dr. Rachel ChenChief Medical Officer, HealthTech Innovations, USA
Verified

Their compliance platform reduced our risk exposure and improved audit readiness across multiple jurisdictions. Most vendors talk about compliance. These engineers build for it.

LT
Lisa ThompsonChief Compliance Officer, Global Enterprises, UK
Verified

The systems are solid, the documentation is thorough, and the team stayed accountable well past the delivery date. That mix of technical depth and post-launch ownership is rare at this level of AI engineering.

ES
Evan SolomonCEO, EFS Networks, USA
Verified

The autonomous delivery platform changed how our engineering org operates. Pipeline failures, debugging, and incident resolution now happen at a speed and consistency that was not achievable before.

DF
Daniel FosterDirector of Engineering, NexaScale Systems, USA
Verified

The program governance platform gave our executive team something we had been missing on every large initiative: real-time visibility into risks, dependencies, and execution gaps before they became problems.

IM
Isabella MullerVP Strategy and Operations, EuroCore Group, Germany
Verified

Their AWS-integrated data science platform cut our model deployment time by 65% and improved prediction accuracy by 38%. The architecture they designed is now the backbone of our entire analytics operation.

SC
Sarah ChenVP of Engineering, TechVentures Global, Germany
Verified

Their n8n lead management automation dropped our lead response time from four hours to under three minutes. The workflows they designed are ones our internal team would never have built on their own.

MR
Marcus ReidCOO, GrowthEdge Partners, Canada
Verified

Our legal contract review platform was built in six weeks. What took paralegals two full days now takes the AI two minutes at 92% accuracy. The NLP expertise and pace of delivery are a rare combination.

HM
Hannah MorrisonDirector of Innovation, LegalEdge UK
Verified

Their AI talent acquisition system cut our time-to-hire by 52% and improved candidate quality scores by 34%. The change management support they provided made rollout across 15 offices completely smooth.

PB
Priya BhatiaCHRO, NexGen Workforce Solutions, Germany
Verified

Our predictive real estate valuation engine was built with Jillani SofTech. Model accuracy beat every commercial provider we had evaluated. The ability to deliver enterprise AI at startup speed is remarkable. They are our exclusive AI partner going forward.

DK
Daniel KowalskiCEO, PropIntel Platform, EU
Verified

They think about AI the way a senior technology architect thinks about enterprise systems. Not tools to bolt on, but infrastructure to build around. The knowledge platform gave us real-time visibility across complex multi-region work in a way nothing prior had managed.

FS
Frank ShinesHead of AI and Digital Transformation, USA
Verified
Technology

The full stack behind every delivery

We pick the right tool for your use case, not the most familiar one. Here is what we work with across all practice areas.

Large Language Models
OpenAI GPT-5.5GPT-5.4Claude Opus 4.8Claude Sonnet 4.6Gemini 3 ProLLaMA 4Mistral Large 3DeepSeek V3Qwen 3Hugging Face
AI Frameworks and Orchestration
LangChainLangGraphLlamaIndexCrewAIAutoGenPhiDataDSPyLangSmith
RAG and Retrieval
RAG FusionAgentic RAGRAGatouilleHybrid Search BM25Cross-Encoder RerankingMetadata FilteringCitation GroundingRAGAS Evaluation
Vector Databases
PineconeWeaviateChromaDBQdrantFAISSpgvectorMilvusRedis VectorElasticsearchNeo4j Graph
Workflow Automation and RPA
n8n self-hostedMake.comZapierPower AutomateUiPathAutomation AnywhereBlue PrismRetoolBubble.io
Cloud Platforms
AWS SageMakerAWS BedrockAWS LambdaAWS EC2Azure MLAzure OpenAIGCP Vertex AIBigQuery MLHerokuVercel
MLOps and LLMOps
MLflowZenMLLangSmithEvidently AIWeights and BiasesPrometheusGrafanaCI/CD Pipelines
Fine-Tuning and Alignment
QLoRALoRAPEFTRLHFDPO AlignmentHugging Face TRLGemma 3 27BLLaMA 4 Fine-tune
Machine Learning Frameworks
PyTorchTensorFlowKerasScikit-learnXGBoostLightGBMOpenCVspaCyYOLOONNX
Data Engineering and Analytics
Apache SparkApache KafkaApache AirflowSnowflakeDatabricksdbtTableauPower BIBigQueryPySpark
Backend, Deployment and DevOps
PythonFastAPIFlaskStreamlitReactNext.jsDockerKubernetesGitHub ActionsJenkinsTerraform
Databases
PostgreSQLMongoDBMySQLRedisNeo4jOpenSearchRedshift
Why Jillani SofTech

Six reasons enterprises keep coming back

The things that separate production AI from prototype AI.

01

22 production systems shipped

Real AI systems in real enterprises across healthcare, finance, legal, retail, and DevOps. Every engagement has documented outcomes. No prototypes that never went live.

02

Triple cloud certified

Certified on AWS, Azure, and GCP. We architect on any major cloud with enterprise-grade security and full compliance with your regulatory environment from day one.

03

100% Upwork job success

Top Rated Plus, the highest tier on Upwork, with a perfect success rate across every engagement. Every client satisfied, every commitment honored.

04

Full stack, one partner

Retrieval architecture, agents, APIs, databases, deployment, and monitoring, all under one engagement. One person accountable. No vendor coordination, no finger-pointing.

05

Compliance from the first decision

HIPAA, GDPR, SOC 2, and CSRD built into the architecture before a single line of code. Not retrofitted at the end, not added to a checklist after launch.

06

Accountable after launch

Monitoring, SLAs, model performance tracking, and quarterly improvement cycles are part of every engagement. We do not build and disappear. We stay.

About Jillani SofTech

Built by an engineer. Run like a product company.

I am Muhammad Ghulam Jillani, a Full Stack AI Engineer, Lead AI Data Scientist, and the founder of Jillani SofTech. I started this company because I kept seeing the same problem: enterprises wanted AI, but vendors kept handing them prototypes that broke the moment real data hit them.

So I built the company around one rule. Nothing ships unless it works in production. Every system we deliver has documented architecture, real monitoring, a clean handoff, and post-launch support. Not as an add-on. As the standard.

Five years and 22 production systems later, I still personally lead the architecture and delivery on every engagement. When you work with us, you work with me directly, not a project manager passing notes between you and a team you have never met.

We are Top Rated Plus on Upwork with a 100% job success rate, triple-certified across AWS, Azure, and GCP, and recognized as a 24x LinkedIn Top Voice in AI. More to the point, 27 enterprise clients have come back for a second engagement, which is the only metric that actually matters.

If you have a workflow that is costing you time and money, data that is sitting unused, or an AI system that is not performing the way it should, book a call. In 30 minutes I will tell you whether we can fix it and what that looks like.

Book a Call with Muhammad Email Directly
22+Production AI Systems
100%Job Success Rate
27+Enterprise Clients
24/7Production Support
5+Years Engineering AI
24xLinkedIn Top Voice in AI
What to Expect

Your first 30 minutes with us

No sales script. A working technical conversation that tells you whether this is even worth doing.

1

You describe the workflow

Tell us the process, the data, and what is slowing you down. We ask the technical questions that actually matter.

2

You get an honest read

Whether AI fits, what is realistic, and what it is not. If it is not worth building, we will tell you that too.

3

You leave with a plan

A clear next step: a scoped approach, a rough timeline, and a price range. No pressure to commit on the call.

Your AI system should work, not just demo well

Book a free 30-minute call. We listen to your use case, ask the technical questions that matter, and give you an honest read on what is realistic, how long it takes, and what it costs.

Book Free Consultation Chat on WhatsApp Email the Engineer

  You own all code and IP  ·  NDA on request  ·  Fixed-scope quotes  ·  We reply within hours

FAQ

Straight answers to common questions

The questions we hear most often before an engagement begins.

What do you actually build?

RAG systems, AI agents, LLM SaaS products, MLOps pipelines, and production ML infrastructure. Every engagement covers the full stack: architecture, development, deployment, documentation, and post-launch support. We do not deliver notebooks or prototypes, only working production systems.

How long does a project take?

A RAG system or n8n workflow usually takes 2 to 4 weeks. A full enterprise platform, multi-agent system, or complete MLOps pipeline takes 8 to 16 weeks. We use agile delivery with a working demo from sprint two onward, so you see real progress throughout.

What does it cost?

A RAG chatbot for internal documents starts from around $3,500. A full enterprise platform with agents, APIs, dashboards, and cloud deployment typically runs from $15,000 to $60,000 or more. We give a detailed estimate after a free 30-minute scoping call.

Do you work with healthcare and financial services?

Yes. We have built HIPAA-compliant clinical AI for a US hospital network, SOC 2 ready financial governance platforms, and GDPR plus CSRD compliance systems for EU enterprises. Security and compliance are built into the architecture from the first design decision.

What makes a RAG system fail?

Almost every RAG failure comes from weak retrieval, not a weak model. Poor chunking, single-vector search without reranking, and no evaluation framework. We build with hybrid search (BM25 plus dense), cross-encoder reranking, citation grounding, and RAGAS evaluation against ground-truth benchmarks before deployment.

Do you support the system after delivery?

Yes. Every engagement includes post-launch monitoring, rapid incident response, and regular performance reviews. For ongoing clients we offer monthly support retainers covering model updates, retrieval tuning, and system improvements. We stay accountable for what we build.

How do I get started?

Book a free 30-minute call at calendly.com/jillanisofttech/30mins, email m.g.jillani@jillanisoftech.com, or message on WhatsApp. On the first call I listen to your use case, ask clarifying questions, and give you an honest read on what is feasible, how long it takes, and what it costs.

Do we own the code and IP?

Yes. On delivery you receive the full source code, documentation, and a complete IP transfer. Everything runs in your own cloud accounts and repositories. There is no lock-in and nothing licensed back to us.

How do you handle data security and confidentiality?

We sign an NDA on request, work under least-privilege access, and never train external models on your data. For regulated work we build HIPAA, GDPR, or SOC 2 controls into the architecture from the first design decision.

Can you work with our existing team and tech stack?

Yes. We embed alongside your engineers, follow your stack and conventions, and document everything for a clean handoff. Team augmentation and architecture reviews are available as part of a retainer.

Contact

Let's talk about your problem

Share your use case and we will tell you in 30 minutes what the right approach looks like.

What we help with

RAG and Document Intelligence

Your documents searched in seconds with citations and access control

AI Agents and Task Automation

Agents that connect to your tools and actually execute business tasks

LLM SaaS and Copilots

Full AI products: APIs, auth, dashboards, database, cloud deploy

MLOps and Cloud AI

Production pipelines, drift detection, model governance on any cloud

Compliance-Ready Architecture

HIPAA, GDPR, SOC 2 built in from day one, not retrofitted

Reach us directly

Phone USA 馃嚭馃嚫

+1-501-420-2439

Phone Pakistan 馃嚨馃嚢

+92-321-1174167

WhatsApp

Message us on WhatsApp now

Email

m.g.jillani@jillanisoftech.com

Book a Call

Free 30-minute consultation

We Serve

USA 路 UK 路 Canada 路 Germany 路 EU 路 Gulf 路 Worldwide

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