Case Studies

12 production AI systems and the numbers they returned

Every figure on this page comes from a system running in production for a real client. No projections, no pilot results presented as outcomes, no invented metrics. Where a confidentiality agreement covers the client name, the sector and region are given instead and the figures are published with permission.

22+Systems in production

27+Enterprise clients

$870K+Documented savings

100%Job success rate

Case Studies

12 production systems, written up in full

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

LegalTechUSA and UKQLoRA · DPO · 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.

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 · AWS SageMaker · SOC 2

One governance layer for 5 production models in a regulated FinTech

The Problem

5 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 5 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.

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 · LangGraph · GPT-5

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

The Problem

14 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.

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 2 years trying to build internally. The results were immediate and measurable."

Michael Stevens, CTO, TechCorp Solutions, USA

HealthcareUSAAgentic RAG · GCP Vertex AI · 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.

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 · RAG Fusion · 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.

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 · Claude Sonnet · GPT-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 5 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.

61%Queries resolved without escalation

44%Reduction in average response time

23%Improvement in audience engagement

24/7Global coverage, 0 added headcount

"Customer volume grew substantially after launch while headcount stayed flat. The AI handles what would have taken 3 full-time agents. It changed our support economics."

VP of Customer Experience, US E-Commerce Brand

RegTechGermany and EUAgentic RAG · Azure OpenAI · LLMOps

Regulatory intelligence across 8 countries and 3 languages

The Problem

A major European enterprise was managing GDPR, EU CSRD sustainability mandates, and internal policy review across 8 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.

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 · AutoGen · GPT-4o

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.

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-4o · LangGraph · Neo4j

Program governance that flags risk before it escalates

The Problem

A global enterprise running 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, and risks surfaced only after they had escalated.

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.

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 · RLHF · Snowflake

2 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. 2 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.

14%Increase in e-commerce conversion rate

31%Improvement in inventory planning accuracy

62%Improvement in demand forecasting accuracy

100msSub-100ms recommendation response

"2 problems we had fought for 3 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

Customer OperationsUSAStreaming ASR · RAG · GCP

A voice agent that handles most of the tier-1 queue without a human

The Problem

A support organisation carried a heavy tier-1 queue of repetitive, well-understood questions. Hold times drove most of the dissatisfaction, the queue peaked exactly when staffing was hardest, and conventional IVR menus just pushed callers to press 0.

What We Built

A real-time voice layer on GCP: streaming speech recognition, retrieval grounded in the client's knowledge base, and a policy layer deciding what the agent may resolve alone. The loop stays under 1 second at the caller's ear; low confidence or asking for a person triggers a warm transfer with the transcript.

architecture / voice-pipeline.svg

measured · voice-ai · prod

80%+Tier-1 calls handled autonomously

<1sResponse latency at the caller

45%Lower average handle time

24/7Coverage with no added headcount

The client's name and sector are under NDA. Every figure above comes from their live production queue and is published with their permission.

Client named under NDA · figures released with permission

Regulated EnterpriseEUOpen weights · Self-hosted · VPC

A private LLM deployment where no request ever leaves the customer environment

The Problem

A regulated enterprise wanted a modern language model over its internal documents, under a contractual ban on sending that content to any third-party processor. Even zero-retention API terms did not clear the bar: nothing could leave the environment at all.

What We Built

A fully private deployment inside the customer VPC: open-weight models on client-owned GPU nodes, self-hosted vector store, orchestration and observability, with egress to external model APIs blocked at the network layer. The audit answer to "where did this data go" is the same as "where does it live".

architecture / private-deployment.svg

measured · private-llm · prod

100%Data residency inside the client VPC

0Requests leaving the environment

OpenWeights, on client-owned hardware

FullAudit trail held internally

The client's name, sector and jurisdiction are under NDA. The residency figure is the measurement they cleared us to publish.

Client named under NDA · figures released with permission

Client Feedback

12 clients, in their own words

Direct quotes from the people we built for. Every testimonial is tied to a real delivery, and reference calls with past clients are available on request.

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.

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.

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.

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.

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.

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.

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.

Sarah ChenVP of Engineering, TechVentures Global, Germany

Verified

Their n8n lead management automation dropped our lead response time from 4 hours to under 3 minutes. The workflows they designed are ones our internal team would never have built on their own.

Marcus ReidCOO, GrowthEdge Partners, Canada

Verified

Our legal contract review platform was built in 6 weeks. What took paralegals 2 full days now takes the AI 2 minutes at 92% accuracy. The NLP expertise and pace of delivery are a rare combination.

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.

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.

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.

Frank ShinesHead of AI and Digital Transformation, USA

Verified

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