Artificial Intelligence
AI that earns its place in production: evaluated, guarded, and tied to a number that matters.
Overview
MostAIprojectsfailinthegapbetweendemoandproduction.Apromptthatimpressesinameetingbehavesunpredictablyagainsttenthousandrealdocuments.Weclosethatgapwithevaluationharnesses,retrievalyoucanaudit,andhumanreviewwherethestakesrequireit.
We work on the problems where language models genuinely change the economics: document-heavy operations, support triage, knowledge retrieval across scattered systems, and internal copilots that shorten expert work from hours to minutes.
- Use-case assessment and business case
- Evaluation datasets and scoring harness
- Retrieval pipeline and prompt architecture
- Production integration with guardrails
- Monitoring for quality, cost, and drift
Benefits
What you get out of it.
Evaluated, not vibes-tested
Golden datasets and regression suites, so you know whether a change made the system better or just different.
Grounded answers
Retrieval over your own sources with citations, keeping responses traceable to a document a person can open.
Guardrails and escalation
Confidence thresholds, PII handling, and clean handoff to a human when the model should not decide alone.
Cost per outcome
Model routing, caching, and prompt hygiene that keep unit economics viable at production volume.
Approach
How a ai engagement runs.
Four stages, each ending in something you can review. Scope stays flexible; the budget and the date do not.
Opportunity mapping
We rank candidate use cases by value, data readiness, and risk, then pick the one worth proving.
Evaluation harness
Before building the feature we build the way we will measure it, using your real data.
Pilot with humans in the loop
A contained rollout where experts review output, and every correction becomes training signal.
Productionise
Monitoring for drift, cost, and latency, with a rollback path and a quarterly model review.
Technologies
- Claude
- Anthropic API
- Python
- TypeScript
- LangGraph
- pgvector
- Supabase
- AWS Bedrock
- Azure AI
- Weights & Biases
Related work
Where this has been applied.
Underwriting Copilot
An evaluated AI assistant that reads submission packs and drafts the first underwriting opinion.
Helix Clinical
A clinical companion app for 1,200 practitioners across 40 sites, built for corridors with no signal.
Vantage Field Ops
Work-order automation and predictive scheduling for 380 field technicians.
FAQ
AI, in practical terms.
The questions clients ask before committing to this kind of work.
AI
Let's talk about ai.
A 45-minute call is usually enough for us both to know whether this is a fit. No deck, no discovery fee for the first conversation.
Or email hello@bonangtech.com