No chatbot bolted onto a homepage. We identify where a model genuinely outperforms a form, then build it with evaluation, guardrails and cost controls from the first prototype.
What you get
Intelligent assistants, retrieval pipelines and custom model integrations embedded directly into your product and operations.
- Opportunity assessment
- Working prototype with evidence
- Retrieval / agent pipeline
- Evaluation suite in CI
- Guardrail + logging layer
- Cost and latency dashboard
2 wks
Prototype to evidence
100%
Evaluated before launch
Your
Data, your tenancy
Six things we do inside every engagement of this type. Not a menu, a standard.
Retrieval systems
Chunking, embeddings, hybrid search and reranking tuned against your corpus, the part that decides whether answers are trustworthy.
Agents & tool use
Models given typed tools and hard boundaries, so actions are auditable and failure modes are contained.
Evaluation harnesses
Golden datasets and automated scoring in CI, so a prompt or model change cannot silently degrade quality.
Guardrails & governance
PII handling, prompt-injection defence, refusal behaviour and full request logging aligned to your compliance posture.
Cost & latency control
Caching, model routing, streaming and token budgets so unit economics work at production volume.
Workflow intelligence
Document extraction, classification, summarisation and triage embedded into the systems your team already uses.
Four principles that shape every decision on this kind of work.
- 01
Find the real use case
We look for tasks that are high-volume, language-shaped and currently expensive. Everything else is a worse version of a database query.
- 02
Prototype against your data
A narrow prototype on real documents within two weeks, enough to prove or kill the idea before the budget grows.
- 03
Instrument quality
Before scaling we build the evaluation set. Without it, 'it seems better' is the only available metric.
- 04
Ship with controls
Rate limits, fallbacks, human review paths and full audit logs go live with the feature, not after the first incident.
Typical stack
Chosen for the problem, not the CV.
We default to boring, well-supported technology and reach for something exotic only when the problem genuinely requires it.
- Claude API
- OpenAI
- LangGraph
- pgvector
- Pinecone
- Python
- TypeScript
No. We use enterprise API tiers with zero-retention or no-training terms, and we document the exact data path before anything is sent. Self-hosted models are an option where policy requires it.
Send us the brief: scope, timeline and budget range. We'll come back with an honest response and a first-pass approach within two working days.