AI features competitors called impossible
Integrated a retrieval-augmented generation pipeline that reduced their manual data entry by 80% and cut support tickets in half.
80%
Less Manual Work
50%
Fewer Support Tickets
97%
Extraction Accuracy
6 weeks
To Full Deployment
What they were up against
Nexloom's team was spending 40+ hours per week manually extracting structured data from unstructured documents — PDFs, emails, and scanned contracts. Their previous AI vendor delivered inconsistent results and their support team was drowning. They needed a custom AI pipeline they could trust.
How we solved it
We built a retrieval-augmented generation pipeline using LangChain and GPT-4, with a custom document chunking strategy tuned for their domain. We integrated Pinecone for vector search and built a human-in-the-loop review interface so their team could validate and correct AI outputs, which fed back into model fine-tuning. Accuracy improved from 62% to 97% over 6 weeks.
Project Timeline
How we delivered it
Every project follows a structured, phased approach. Here's how we took this from kickoff to production.
Data Audit
1 week
Analysed 5,000 sample documents, mapped extraction targets
Pipeline Build
2 weeks
Chunking strategy, embeddings, Pinecone indexing, GPT-4 layer
Evaluate & Tune
2 weeks
Ground-truth benchmarking, prompt optimisation, accuracy tuning
Production Deploy
1 week
Monitoring, HITL review interface, drift detection setup
Data Audit
1 week
Analysed 5,000 sample documents, mapped extraction targets
Pipeline Build
2 weeks
Chunking strategy, embeddings, Pinecone indexing, GPT-4 layer
Evaluate & Tune
2 weeks
Ground-truth benchmarking, prompt optimisation, accuracy tuning
Production Deploy
1 week
Monitoring, HITL review interface, drift detection setup
Transformation
Before & after working with us
The measurable shift — from where Nexloom started to where they ended up.
Technologies Used
Built with a modern stack
We chose each technology for a reason — optimising for performance, developer experience, and long-term maintainability.
Deliverables
What we shipped
A breakdown of the key features and systems we designed, built, and deployed for Nexloom.
Business Impact
Measurable outcomes
The numbers that matter — real business results our client achieved after launch.
80% less manual work
Labour Savings
50% fewer tickets
Support Load
62% → 97%
Accuracy
“Their AI integration expertise is genuinely rare. They shipped features our previous agency said were technically impossible. Total game changer.”
Priya Nair
CTO, Nexloom
Insights
Key learnings from this project
Hard-won insights our team took away — the kind of knowledge that only comes from building in production.
Domain-specific chunking was the single biggest accuracy lever — generic chunking strategies missed critical context boundaries.
Human-in-the-loop review isn't a crutch — it's a training data pipeline that makes the AI get smarter over time.
Building an evaluation framework before writing AI code prevented weeks of undirected prompt engineering.
More Work
Related case studies
Explore more projects where we solved similar challenges.
FAQ
Common questions answered
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