Do you fine-tune models?

Only when evals show retrieval and prompting have plateaued. Most production systems do not need it, and we say so in writing.

How do you keep customer data out of the model provider?

Private endpoints inside your cloud account, PII redaction before any call and a data-handling record your legal team reviews before launch.

How do you measure whether the answers are right?

An evaluation suite built from your own questions and reference answers, scored for faithfulness, relevance and refusal behavior on every change.

What does a query cost?

It depends on context size and model, which is why every build ships with a per-query cost dashboard and a ceiling you set.

AI Engineering

Take AI from a demo to a production system with evals, guardrails and a cost ceiling.

Use-case review, retrieval over your documents, agents, evaluation suites, PII guardrails and LLMOps on Bedrock, Azure OpenAI or Vertex.

Explore AI use cases for your industry Book a discovery call

0%
answer faithfulness on evalsexample: $0.000
per queryexample: 0.0s
p95 latencyexample

The problem we solve

The demo impressed the board and never shipped

Production AI is an engineering problem: data readiness, retrieval quality, evaluation, guardrails and cost. We build the system around the model so the answer quality is measured, the data stays inside your boundary and the invoice is predictable.

“We have an AI demo that never shipped.”
“Legal will not approve anything that can leak customer data.”
“Nobody can tell me what a query costs or whether the answers are right.”

What we deliver

Deliverables, not decks

Use-case and data readiness review
Retrieval-augmented generation over company documents
Agents and workflow automation with human checkpoints
Model evaluation suites and red-teaming
PII redaction and guardrails
LLMOps on AWS Bedrock, Azure OpenAI or Google Vertex
Fine-tuning when it pays off, and a written reason when it does not

How an engagement runs

Fixed scope first, then sprints

Review Weeks 1 to 2
Use-case scoring, data readiness, cost ceiling and an eval plan.
Prototype Weeks 3 to 5
Retrieval pipeline, baseline evals and a working interface on real data.
Harden Weeks 6 to 9
Guardrails, PII handling, red-team pass, latency and cost tuning.
Operate Week 10 onward
Eval dashboard, drift alerts and a monthly quality review.

First engagement: AI use-case and data readiness review, 2 weeks, written decision record included. See the full process.

Proof

What this looks like in production

Read the example case study for this practice, then run the tool below with your own numbers.

Read the example case study

Tools and platforms

AWS Bedrock
Azure OpenAI
Google Vertex AI
Claude
LangGraph
pgvector
OpenSearch
Ragas
Langfuse
Presidio

eval run

$ icdatt eval run --suite dispatch-qa --model claude-sonnetloading 340 reference questions ... okretrieval  recall@5   0.91answers    faithfulness   0.92   relevance 0.89safety     pii-leak 0 / 340   refusal-correct 38 / 40cost       $0.004 / query   p95 1.8sregression vs last run: +0.03 faithfulness$ ▌

Interactive

AI use-case explorer

Pick your industry and department. See concrete use cases with impact, data needed, complexity and a reference architecture for each.

Questions we get

Before you book

Works well with

Next step

Talk to the ai practice lead

A 30-minute call about ai engineering: your estate, your deadline, and whether the first engagement fits. We reply within one business day.

Book a discovery call

30-minute call, no slides. We reply within one business day.