Tell us what your institution actually needs.
Send us your requirement. We respond fast, price transparently, and tell you honestly whether AI genuinely helps your problem — or whether you're better off without it.
AI Integration · 2026
AI document Q&A for legal-tech SaaS (anonymised)
Rebuilt a retrofit GPT Q&A feature into an AI-native pipeline: pgvector retrieval, reranking, structured outputs, eval harness, LLM gateway with caching. Cost per question dropped 74%, latency dropped 77%, hallucination rate fell below 1%.
Section 01Problem
What we were asked to solve.
Client had a retrofit GPT wrapper bolted onto their existing legal research product. It was slow, expensive, and had a visible hallucination rate. They were considering abandoning the feature.
Section 02Approach
How we engineered it.
Three-week engagement. Week 1: migrated document embeddings into pgvector, designed reranking pipeline, built 40-case eval set. Week 2: structured output migration, LLM gateway with caching and model routing. Week 3: production cut-over with shadow-mode comparison for two days, then full rollout with feature flag.
Section 03Stack
What we built with.
·Next.js
·Postgres + pgvector
·LangChain
·Langfuse
Section 04Outcome
What shipped.
Result 01
Cost per question: $0.08 → $0.021 (74% reduction)
Result 02
Median latency: 4s → 900ms (77% reduction)
Result 03
Hallucination rate: 6% → under 1% (over 8 weeks post-launch)
Result 04
Monthly AI bill: $4,800 → $1,260
Result 05
40 evals shipped in CI, preventing regressions
CorrespondenceGet in touch