Retrieval over your own data
Document ingestion, chunking, embeddings and reranking tuned to your corpus — with citations so answers can be checked.
Intelligence & data
AI features that survive contact with users.
Retrieval, extraction, classification and agents — built with evaluation, guardrails and cost controls, not a demo that impresses once.
Discuss this project
4–10 weeks depending on data readiness
The short version
Getting an impressive AI demo takes an afternoon. Getting one that's accurate on your data, doesn't leak between customers, doesn't cost more than it saves, and degrades sensibly when the model has a bad day — that's the actual work.
We treat AI features like any other production system. There's an evaluation set before launch, a measured baseline, logging on every call, cost ceilings, and a fallback path for when the provider has an outage. If a simpler approach beats a model, we'll tell you that too.
Typically built with
Python TypeScript LLM APIs PyTorch Vector DBs LangGraph Kubernetes
What's included
How we approach ai & machine learning
Evaluation before it ships
A graded test set and a measured baseline, so quality is a number you can track instead of a vibe.
Guardrails and cost control
Prompt injection defences, output validation, per-tenant isolation, rate limits and hard spend ceilings.
Human in the loop where it matters
Confidence thresholds that route edge cases to a person instead of quietly guessing.
What you get
Delivered at the end
- Production AI feature integrated into your product
- Evaluation suite and quality baseline you can re-run
- Cost, latency and accuracy dashboards
- Written notes on model choice, limits and failure modes
Right fit
This is for you if
- 01 Teams drowning in documents, tickets or manual review
- 02 Products where search or support is the bottleneck
- 03 Companies with an AI prototype that stalled before production
Not quite what you need? Tell us the outcome you're after — if a different service or a smaller piece of work would get you there faster, we'll say so.
Ask us insteadHow we work
A clear path from idea to impact
- 01
Discover
We map your goals, users, and constraints into a sharp, prioritized scope.
- 02
Design
Architecture and interfaces are prototyped and validated before a line ships.
- 03
Build
Engineers deliver in tight sprints — you see working software every week.
- 04
Scale
We launch, monitor, and harden — then grow the system alongside your business.
Questions
What clients ask about ai & machine learning
The things that come up most on the first call. Anything else, just ask — we answer straight.
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Will our data be used to train someone's model?
Not unless you choose a provider that does. We default to enterprise API tiers with no training on customer data, and we'll confirm the exact terms of whichever provider you pick in writing.
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What if the model gets it wrong?
We design for that from the start: confidence thresholds, human review on low-confidence cases, output validation, and citations so a person can verify an answer instead of trusting it blindly.
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How do we keep costs predictable?
Caching, smaller models for the easy cases, and hard per-tenant spend ceilings. You get a cost-per-request dashboard so a pricing change never arrives as a surprise invoice.
Often paired with
All servicesData Analytics & Engineering
Pipelines, warehouses, and dashboards that turn raw data into decisions.
View serviceWeb App Development
Fast, secure, scalable web applications with modern frameworks and clean architecture.
View serviceCustom Software Development
Tailored platforms engineered around your exact workflows — not off-the-shelf compromises.
View serviceNext step
Let's scope ai & machine learning
Thirty minutes with the engineer who'd lead it. You leave with a scope, a number, and an honest read on whether it's worth building.
Prefer email? support@zeetek.net