AI

AI features that pass review, not just demo well

There is a wide gap between an AI prototype that impresses a board and an AI feature that runs safely against customer data at volume. The gap is evaluation, guardrails, latency and cost. That gap is most of the work, and it is where we spend our time.

Scope

What this covers

Terminal output and code on a developer workstation
01

Retrieval augmented generation

Document ingestion, chunking strategy, hybrid search, reranking and citation. Built so answers can be traced back to a source, which is what makes the feature usable in regulated settings.

02

Agentic workflows

Systems where a model plans, calls tools and acts, with bounded permissions, human approval gates on anything irreversible and full traces of every step.

03

Document and data extraction

Turning contracts, claims, invoices and clinical notes into structured records, with confidence scores and a review queue for the cases the model should not decide alone.

04

Evaluation and observability

Golden datasets, regression suites that run in CI, per request cost and latency tracking, and dashboards that show quality drift before your users report it.

05

Model strategy and cost control

Routing between model tiers, caching, prompt compression and honest advice about when a smaller model or plain code beats a large one.

Stack

What we build it with

Tools are chosen per project and justified in an architecture decision record. This is what we reach for most often in this practice.

Claude and the Anthropic APIOpenAI APIModel Context ProtocolLangGraphpgvector and QdrantPythonTypeScriptRagas and custom eval harnessesOpenTelemetry

Deliverables

What you actually receive

  • Evaluation harness with a versioned golden dataset
  • Guardrail policy covering prompt injection and data leakage
  • Per request cost and latency dashboard
  • Fallback behaviour for model outage and degraded quality
  • Written model selection rationale you can revisit later

Questions

What clients ask first

Will our data be used to train a model?
Not under the arrangements we set up. We use enterprise API tiers with no training on inputs, and where policy requires it we deploy inside your own cloud tenancy.
How do you stop the model making things up?
Retrieval with mandatory citation, structured output schemas, confidence thresholds that route low confidence cases to a human, and a regression suite that catches quality drops before release. Nothing eliminates it entirely, and any vendor who tells you otherwise is selling.
What does it cost to run?
We model per request cost during the architecture sprint and design to a target. On most document workloads we land between a fraction of a cent and a few cents per request after caching and routing.

Need agentic and generative ai?

Send a short brief and a senior engineer will read it. You get a written response with our honest read on scope, risk and cost within one business day. No discovery call required to get a real answer.

  • A senior engineer reads every brief
  • NDA signed before you share anything sensitive
  • No sales sequence, no automated follow ups