Retrieval and context pipelines
Indexing, chunking and refresh with an explicit answer to how stale a result is allowed to be.
Service
AIDevelopment
Model-backed features built like the rest of your product.
Running
- RAG systems
- LLM features
- Evals in CI
- Cost & latency
Theproblem
The demo isthe easy part
A feature that answers well in a meeting can fail in production for reasons unrelated to the model: retrieval gone stale, a prompt edited without review, a spend line nobody forecast, a failure mode with no fallback.
Mustafa Tariq
AI Engineer · leads this service
Our approach
Retrieval with a freshness contract, prompts under version control, evals that block a bad merge, and cost and latency instrumented from the first deploy. These are the parts that decide whether a feature survives its first quarter.
What youget
- Eval set and harness agreed before the build starts
- Production feature with guardrails and fallbacks
- Cost, latency and quality dashboards
- Operational notes for the parts that will drift
- Prompt changes
- Reviewed
- Regressions
- Blocked in CI
- Spend
- Attributable
Capabilities
What AI Developmentincludes
Scoped per engagement. We start with whichever of these removes the biggest constraint first.
Prompts and evals in CI
Prompt changes go through review and run against an eval set, so a regression is caught before merge rather than by a customer.
Guardrails and fallbacks
Output validation, rate limiting, and defined behaviour for when the model is slow, wrong, or unavailable.
Agents and tool use
Multi-step workflows with bounded autonomy, explicit tool permissions and a full trace of what the agent did.
Cost and latency instrumentation
Spend and p95 broken out per feature and per customer, readable before finance asks about the bill.
Model portability
An abstraction that lets you change provider without rewriting the feature, plus benchmarks to justify the switch.
Delivery
Howa project runs
Typical shape for this service. Timings move with scope, the order does not.
- 01
Define done
Week 1
We agree what correct output looks like and build the eval set before building the feature.
- 02
Build thin
Week 1–4
The narrowest version that touches production, instrumented from the first deploy.
- 03
Harden
Week 4–8
Failure modes, fallbacks and cost controls driven by what the thin version actually did.
- 04
Hand over
Ongoing
Eval harness, dashboards and the operational notes your team will need in month six.
Typicalstack
Tools we reach for
Chosen per engagement and biased toward what your team can maintain after we leave.
- TypeScript
- Next.js
- Python
- LLM APIs
- OpenTelemetry
Commonquestions
The questions we getabout Development
Which model providers do you use?
Whichever benchmarks best for the task and fits your data constraints. We build so the provider can change without a rewrite.
How do you stop hallucinations?
You reduce and detect them: grounded retrieval, output validation, confidence thresholds and evals that measure the rate. Anyone promising elimination is selling something.
Can you work inside our existing codebase?
Yes, that is the usual case. We work in your repository, in your review process.
Starthere
Tell us the process,not the solution.
The most useful first message describes what someone on your team does by hand today and how often. That is enough for us to tell you whether it is worth building.
What happens next
- A named engineer reads it, not a form inbox
- Reply within 24 hours, even if we're not the right fit
- A 30-minute call to trace the process end to end
- A fixed-scope quote, or an honest no