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Retrieval design
Chunking, embedding, and ranking chosen against your actual corpus and measured on real questions. Most disappointing answers are a retrieval problem wearing a model problem's clothes.
SERVICE · AI
AI Engineering
A language model call is easy. The system around it is the work: knowing which documents to retrieve, what the model is allowed to do, how you tell whether an answer was good, what happens when the provider is slow, and how you reconstruct a decision six months later when somebody asks. We build that system, and we measure it.
THE PROBLEM
The gap between a demo and a deployment is where most AI projects stop.
A prototype is judged by a person who wants it to work, on inputs they chose, once. Production is judged by whoever gets the worst output of the week, on inputs nobody anticipated, continuously. The techniques that close that gap are not model tricks — they are retrieval quality, evaluation you can run on every change, guardrails at the boundaries, fallbacks for when a provider degrades, and enough recorded context to answer why a specific decision was made. Teams that skip these do not find out the system is unreliable; they find out they cannot tell whether it is.
Signals you might recognize
The demo worked and the pilot did not, and the difference has never been diagnosed.
Output quality varies between runs and nobody can say what changed.
You cannot tell an auditor, a regulator, or a customer how a particular decision was reached.
Costs are unpredictable because nothing measures tokens per completed task.
WHAT WE DELIVER
What this actually includes.
Concrete engineering capabilities deployed as part of this service practice.
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Agent and tool orchestration
Explicit tools with typed inputs and outputs, bounded permissions, and defined failure behaviour. The model chooses between actions; it does not invent them.
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Evaluation harnesses
A graded set of real cases that runs on every change, so a prompt edit that improves one thing and breaks two others is caught before it ships rather than reported by a user.
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Guardrails and fallbacks
Input and output validation, refusal handling, and defined behaviour when a provider is slow, rate-limited, or down. Degraded is a designed state, not an outage.
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Cost and latency work
Measurement per completed task rather than per call, then model routing, caching, and prompt reduction against the numbers. Latency budgets set before the interface is designed.
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Observability and traceability
Every step recorded: what was retrieved, what was sent, what came back, what it cost. This is what makes a decision explainable after the fact instead of unreproducible.
HOW WE WORK
How we approach AI Engineering.
Milestone-driven delivery, so integration problems surface in week two rather than in week ten.
01
Decide whether a model belongs here
Some of the tasks brought to us are better served by a query, a rule, or a form. We say so. The tasks that genuinely suit a language model are the ones with judgment, ambiguity, or unstructured input in the middle, and those are worth doing properly.
Stage 01 activities
- Task analysis against a deterministic alternative
- A definition of what a good output looks like, written down
- Data and access review, including what cannot leave your systems
- An honest read on expected accuracy and what it would cost to raise it
02
Build the evaluation set first
We collect real cases with graded expected outputs before writing the pipeline. Without it, every subsequent change is judged by whoever tried it last, and improvement becomes a matter of opinion.
Stage 02 activities
- Case collection from real usage or domain experts
- Grading criteria per case, including the failure modes that matter
- An automated scoring run wired into the repository
- A baseline score, recorded before any tuning
03
Build retrieval and orchestration
Ingestion, chunking, embedding, and ranking, then the tool layer around the model. Each configuration change is scored against the evaluation set rather than argued about.
Stage 03 activities
- Ingestion pipeline with incremental updates and re-indexing
- Retrieval tuning measured, not guessed
- Typed tool definitions with bounded permissions
- Provider abstraction so a model can be swapped by configuration
04
Harden the edges
The failure paths get as much attention as the happy one: validation on the way in and out, timeouts, retries with sensible backoff, rate-limit behaviour, and a defined answer for the case where the model cannot help.
Stage 04 activities
- Input and output schema validation
- Timeout, retry, and circuit-breaker behaviour per provider
- A designed degraded mode rather than an error page
- Human escalation path where the stakes require one
05
Instrument, then operate
Traces, cost per completed task, and quality scores on live traffic, plus a way to feed real failures back into the evaluation set. A system that is measured keeps improving; one that is not decays quietly.
Stage 05 activities
- Per-request tracing with retrieval and prompt context retained
- Cost and latency dashboards at task level
- Live quality sampling with a review workflow
- A loop that turns reported failures into new evaluation cases
TECHNOLOGY DEPTH
Technologies behind AI Engineering.
The libraries, runtimes, and services we standardise on for this work, and keep patched.
Frontend
- TypeScript
Backend
- Python
- Node.js
Data
- Vector search
- PostgreSQL
- Redis
Cloud
- Docker
AI
- OpenAI
- Anthropic
- LangChain
DELIVERABLES & OUTCOMES
What you receive, and what actually changes.
We measure success by durable working software in your possession, not presentation slides.
What you get
A graded evaluation set and an automated scoring run
Retrieval pipeline with documented tuning and measured results
Tool and orchestration layer with typed contracts
Provider abstraction covering at least two model vendors
Tracing, cost, and quality instrumentation in production
A written account of accuracy, known failure modes, and their handling
What changes
A quality number you can point at, and a way to tell whether a change improved it
The ability to reconstruct any individual decision the system made
Predictable cost per completed task rather than a surprising monthly bill
Defined behaviour when a model provider degrades, instead of an outage
QUESTIONS & ANSWERS
Questions about AI Engineering.
Direct answers to common technical and engagement questions.
Not necessarily. Retrieval keeps most content in your systems and sends only the passages needed for a specific question. Where that is still unacceptable, self-hosted models are workable for some tasks, at a real cost in quality and operational effort. We will tell you which category your use case falls into before you commit.
With a graded set of real cases and expected outputs, scored automatically on every change. It is unglamorous and it is the difference between improving a system and rearranging it. We build this before the pipeline, because retrofitting it means re-deriving what good looked like.
Retrieval grounding, output validation against a schema, citation of the source passage, and a designed path for the case where the model genuinely does not have the answer. No arrangement of these removes the failure mode entirely, so we also make the residual rate visible rather than implying it is zero.
Whichever suits the task, and the system is built so that changing it is a configuration change. Model quality, pricing, and rate limits all move faster than any project timeline, so committing the architecture to one vendor is a risk with no matching benefit.
Often, yes, and the first step is usually the same: build the evaluation set for what exists today. That alone tends to explain the variance people are seeing, and it turns the rest of the work into measurable changes rather than a rebuild.
Have something ambitious to build?
Tell us what you're working on. We'll tell you honestly whether we're the right team for it.