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Teams that have shipped to production with us

  • TechCorp SolutionsEnterprise SaaS, USA
  • HealthTech InnovationsHealthcare, USA
  • EFS NetworksFinancial services, USA
  • NexaScale SystemsDevOps, USA
  • EuroCore GroupEnterprise, Germany
  • PropIntelReal estate, EU
  • LegalEdge UKLegalTech, UK
  • TechVentures GlobalAnalytics, Germany
  • GrowthEdge PartnersB2B services, Canada
  • NexGen WorkforceHR technology, Germany
  • TechCorp SolutionsEnterprise SaaS, USA
  • HealthTech InnovationsHealthcare, USA
  • EFS NetworksFinancial services, USA
  • NexaScale SystemsDevOps, USA
  • EuroCore GroupEnterprise, Germany
  • PropIntelReal estate, EU
  • LegalEdge UKLegalTech, UK
  • TechVentures GlobalAnalytics, Germany
  • GrowthEdge PartnersB2B services, Canada
  • NexGen WorkforceHR technology, Germany

The short version

An AI engineering firm that is judged on shipped systems

Founder-led, fully remote, working in your time zone whether that is the USA, the UK or the EU. Every figure below comes from a deployed system with a client attached.

22+

Production AI systems delivered

27+

Enterprise clients served

$870K

Client costs saved and documented

100%

Job success rate, public on Upwork

Is this on your desk?

4 problems we are usually called in to fix

Your pilot impressed leadership, then stalled on cost, latency or a compliance review.

Add the evaluation set, guardrails, tracing and monitoring a reviewer needs to sign off, then put real users on it.

Your LLM bill is growing faster than your usage.

Model routing, semantic caching and private open-weight models where the volume justifies them. 1 legal client now runs at 60% lower inference cost.

Nobody can tell you whether the system got worse last week.

Evaluation gates in CI and drift detection on live traffic, so a quality drop blocks the release before a customer sees it.

Your data is not allowed to leave your network.

Open-weight models fine-tuned and served inside your VPC or on premises, with a full audit trail and role-based access.

1 of these on your desk right now? Get on a call today. In 30 minutes you will know the approach we would take and roughly what it costs, whether or not you hire us.

Book today

How a system is put together

The retrieval stack behind every answer we ship

Hybrid search, cross-encoder reranking and a grounding gate that refuses to answer without a citation. The same shape runs in the clinical, legal and compliance platforms on the case studies page.

swipe to see the full diagram

DocumentsPDF · wiki · CRM · ticketsIngestionstructure-aware chunkingHybrid indexBM25 + dense vectorsUser queryRBAC scopedRetrieve + rerankcross-encoder, top-kGrounding gatecitation check · evalLLM answercited, traced, logged
gatemodelstore / IO

How we work

A delivery process built for production, with a working demo from sprint 2

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

01

Scope and KPIs

Week 0

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.

02

Architecture and plan

Week 1

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

03

Build and demo

Weeks 2 to N

Agile sprints with a working demo from sprint 2 onward. Weekly updates, plain reporting on blockers, and evaluation against the KPIs we set.

04

Deploy and hand off

Launch and beyond

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

Straight answers

Sometimes the right answer is that you do not need a large language model

Most AI vendors have 1 hammer. We have shipped enough production systems to know that a generative model is the wrong tool at least as often as it is the right one, and we say so on the first call rather than 3 months into a build. Structured prediction, demand forecasting, credit and fraud scoring and anomaly detection usually go to a gradient boosted model on clean features: better accuracy, lower latency, lower cost, and it passes an audit far more easily. Read the decision guide.

Your problemWhat we would recommend
Answering questions over documentsRAG with hybrid retrieval and citations
Predicting churn, demand or riskClassical ML on your tabular data
Multi-step work across your toolsAn agent with approval gates
Extracting fields from fixed formsDocument AI, not an LLM call per page
Deterministic rules and thresholdsPlain software, no model at all
Domain tone and vocabulary at scaleFine-tuning with held-out evaluation

Selected work

3 systems, 3 sets of numbers

No figure appears on this site unless it came out of a deployed system with a client attached.

Multinational enterpriseMulti-region · AWS

Search and automation across 5 million files that cannot leave their regions

1. Problem. 5 million+ files across regional data centers, S3, document systems, databases and application servers: contracts, financial statements, engineering reports, spreadsheets and email. The usual fix is 1 central index, and regional data rules ruled it out. So every cross-region question, and every workflow touching more than 1 system, depended on someone stitching answers together by hand.

2. Solution. The data stays in its region and the question travels to it. Retrieval runs locally and returns only what the asking user is already allowed to open. 7 LangGraph agents split the work, hand off to each other over A2A and reach each system through MCP servers with scoped permissions. Anything that changes a record waits for human approval, and n8n keeps ingestion, approvals and notifications moving around the clock.

LangGraphA2AMCPn8nOpenSearchLangSmith

Client name withheld under NDA. Ongoing 12-month program; figures are from the agreed evaluation set and the delivered milestones.

3. Impact

5M+
Files searchable across regions, permissions intact
97.86%
Task-success benchmark target on the agreed evaluation set
7
Specialised agents coordinating over A2A
12 months
Ongoing program: 6 delivered, 6 more contracted
HealthcareUSA

HIPAA-compliant clinical decision support for a hospital network

1. 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.

2. Solution. 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.

Agentic RAGGCP Vertex AIHIPAAFHIR

3. Impact

91%
Factual accuracy in clinical responses
27%
Reduction in patient onboarding time
48%
Less manual clinical documentation
HIPAA
Compliance built into the architecture

Client feedback

What clients say after we deliver

Every testimonial is tied to a real delivery. The Upwork reviews behind them are public, written by clients, and not editable by us.

5.0across 19 completed jobsverify on Upwork
“Their revenue platform gave us the pipeline intelligence we had spent 2 years trying to build internally. The results were immediate and measurable. I would recommend them for any serious enterprise AI initiative.”
MS

Michael Stevens

CTO, TechCorp Solutions · USA

  • “Most vendors talk about compliance. These engineers build for it.”

    Lisa Thompson · Chief Compliance Officer, Global Enterprises, UK

  • “The systems are solid, the documentation is thorough, and the team stayed accountable well past the delivery date.”

    Evan Solomon · CEO, EFS Networks, USA

  • “Pipeline failures, debugging, and incident resolution now happen at a speed and consistency that was not achievable before.”

    Daniel Foster · Director of Engineering, NexaScale Systems, USA

  • “Real-time visibility into risks, dependencies, and execution gaps before they became problems.”

    Isabella Muller · VP Strategy and Operations, EuroCore Group, Germany

  • “Cut our model deployment time by 65% and improved prediction accuracy by 38%. Now the backbone of our analytics operation.”

    Sarah Chen · VP of Engineering, TechVentures Global, Germany

  • “Lead response time dropped from 4 hours to under 3 minutes. Workflows our internal team would never have built on their own.”

    Marcus Reid · COO, GrowthEdge Partners, Canada

  • “What took paralegals 2 full days now takes the AI 2 minutes at 92% accuracy. Built in 6 weeks.”

    Hannah Morrison · Director of Innovation, LegalEdge UK, UK

  • “Cut our time-to-hire by 52%. The change management support made rollout across 15 offices completely smooth.”

    Priya Bhatia · CHRO, NexGen Workforce Solutions, Germany

  • “They think about AI the way a senior technology architect thinks about enterprise systems.”

    Frank Shines · Head of AI and Digital Transformation, USA

  • “Most vendors talk about compliance. These engineers build for it.”

    Lisa Thompson · Chief Compliance Officer, Global Enterprises, UK

  • “The systems are solid, the documentation is thorough, and the team stayed accountable well past the delivery date.”

    Evan Solomon · CEO, EFS Networks, USA

  • “Pipeline failures, debugging, and incident resolution now happen at a speed and consistency that was not achievable before.”

    Daniel Foster · Director of Engineering, NexaScale Systems, USA

  • “Real-time visibility into risks, dependencies, and execution gaps before they became problems.”

    Isabella Muller · VP Strategy and Operations, EuroCore Group, Germany

  • “Cut our model deployment time by 65% and improved prediction accuracy by 38%. Now the backbone of our analytics operation.”

    Sarah Chen · VP of Engineering, TechVentures Global, Germany

  • “Lead response time dropped from 4 hours to under 3 minutes. Workflows our internal team would never have built on their own.”

    Marcus Reid · COO, GrowthEdge Partners, Canada

  • “What took paralegals 2 full days now takes the AI 2 minutes at 92% accuracy. Built in 6 weeks.”

    Hannah Morrison · Director of Innovation, LegalEdge UK, UK

  • “Cut our time-to-hire by 52%. The change management support made rollout across 15 offices completely smooth.”

    Priya Bhatia · CHRO, NexGen Workforce Solutions, Germany

  • “They think about AI the way a senior technology architect thinks about enterprise systems.”

    Frank Shines · Head of AI and Digital Transformation, USA

Who we build for

8 sectors, 1 delivery discipline

Regulation, data shape and the cost of a wrong answer drive the architecture, so we scope against your sector's constraints rather than a generic template.

Banking and Finance
Healthcare
Legal and Compliance
Retail and E-Commerce
SaaS and Technology
Manufacturing
Real Estate
Human Resources

Pricing

Clear scope, clear pricing, no surprises at invoice time

4 engagement models, every one ending with a working system, documentation and code you own. Final pricing is confirmed in writing after a free scoping call.

Architecture Review

from $2,500

A written plan in hand before committing to a build. 2 weeks, fixed price.

RAG or Automation Build

from $3,500

A focused system that solves 1 clear problem, shipped in 2 to 4 weeks.

Enterprise AI Platform

$15K to $60K+

A full product with agents, APIs, dashboards and governance, 8 to 16 weeks.

Retainer

$4.8K to $6K monthly

Ongoing engineering capacity embedded in your team, 6 months or longer.

What is included every time, and what moves the number

In-house platforms

The gateway and governance layers are our own code, open to your engineers

3 platforms we built and run, reused inside client engagements so the first milestone lands in weeks. Each one runs end to end with no API key, and your engineers can walk through the code before you sign.

Muhammad Ghulam Jillani

Muhammad Ghulam Jillani

Founder and CEO, Principal Generative AI Architect

Who you work with

The engineer you talk to is the engineer who builds it

Jillani SofTech is a founder-led AI engineering consultancy. Muhammad Ghulam Jillani designs the system, writes the core, and runs the deployment on every engagement, with a small team of AI engineers alongside him and no account managers in between.

  • Founder-led delivery

    6+ years in production AI, Top 100 Global Kaggle Master, 24x LinkedIn Top Voice in AI.

  • A company, not a contractor

    Registered IT services firm. MSA and NDA on request, USD invoicing, milestone payments in writing.

  • Your time zone

    Fully remote across the USA, UK, Germany and the EU, with a fixed weekly checkpoint set to your calendar.

  • Verifiable credentials

    Claude Certified Architect, Professional and Foundations, both issued by Anthropic and verifiable on Credly. Upwork history public, plus AWS, Azure and GCP professional certificates.