Services

Artificial Intelligence Development

AI that survives contact with production — document extraction, classification, retrieval and automation, built with evaluation, monitoring and human oversight designed in.

AI engineered for production

Understanding artificial intelligence 1 — Anawaz

AI should be engineered around measurable outcomes and controlled failure modes, not added because it is expected. Most pilots that stall do so because nobody defined what happens when the model is wrong.

  • Scoped by cost of error – Every engagement starts by pricing a wrong answer — that sets the review model, guardrails and rollout pace.

  • Evaluation before rollout – A measured baseline and a test set decide whether the system ships, not a demo.

  • Retrieval over retraining – Most business cases are knowledge problems — we build retrieval over your own documents before considering fine-tuning.

  • Observability built in – Logged inputs and outputs, cost per request and drift monitoring from the first release.

  • Failure paths designed – Confidence thresholds, human review queues and safe fallbacks for the cases the model gets wrong.

We scope AI work against one question: how expensive is a wrong answer, and how quickly is it caught? That determines whether a use case belongs in production, what validation it needs, and how much human oversight has to stay in the loop.

What we build

  • Document processing — extracting structured fields from invoices, contracts and forms, with deterministic validation so impossible values are caught rather than passed downstream.
  • Classification and routing — tickets, enquiries and documents, measured against a labelled test set before deployment.
  • Retrieval over internal knowledge — answers that cite sources and respect existing access permissions rather than flattening them.
  • Assisted drafting — where a human reviewer is genuinely part of the workflow, not a rubber stamp.
  • Integration into existing applications — the model is rarely the hard part; entitlements, retention, monitoring and cost control usually are.

Evaluation, observability and cost

Three things decide whether an AI system survives its first year in production:

  • Evaluation — a set of real examples with known-correct answers, built before the system, so changes are measured rather than felt.
  • Observability — logging inputs, outputs and human corrections. Those corrections are the most valuable signal you will collect.
  • Cost control — modelled at production volume, not pilot volume, since the ratio between them frequently reverses the business case.

Where source data needs collecting or structuring first, that is a data engineering problem before it is an AI one.

Human oversight and failure modes

We design the failure path before the happy path: what happens when the model is unsure, who sees it, and how a wrong answer is corrected. Oversight is reduced deliberately and measurably once the data supports it — not assumed away at launch.

Discuss an AI Use Case

Start with a use case where a wrong answer is cheap and quickly caught. Whether you want to automate processes, enhance decision-making, or personalize customer experiences, Anawaz has the expertise to bring AI into your business strategy.

Talk to an Engineer

Let’s explore AI solutions tailored to your needs!

Where AI actually holds up

We scope AI work by one question: how expensive is a wrong answer, and how fast is it caught? Use cases where errors are cheap and visible succeed. Where they are expensive and invisible, they fail late — after the pilot was declared a success.

  • Document extraction — pulling structured fields from invoices, contracts and forms, with deterministic validation so impossible values are caught automatically.
  • Classification and routing — tickets, enquiries and documents, measurable against a labelled test set before it goes anywhere near production.
  • Retrieval over internal knowledge — answers that cite their sources, and that respect your existing access permissions rather than flattening them.
  • Drafting with a human reviewer — where the review step is genuinely part of the workflow, not a rubber stamp.

What we build in from the start

  • An evaluation set of real examples with known-correct answers, built before the system, so improvements are measured rather than felt.
  • A defined failure path — what happens when the model is unsure or wrong, who sees it, and how it gets corrected.
  • Logging of inputs, outputs and human corrections, because those corrections are the most valuable training signal you will ever collect.
  • Cost modelling at real production volume, not pilot volume.
  • Graceful degradation when a provider has an outage.

Common questions

Do we need to fine-tune a model?
Usually not. Most cases that look like behaviour problems are knowledge problems, solved better by retrieval over your own documentation. We diagnose using your real failing examples before recommending training.

Will our data be used to train someone else’s model?
That depends on the provider and plan, and it is a question we settle explicitly in writing before any data moves.

Can this run on our own infrastructure?
Often yes, and for sensitive data it is sometimes the right answer. It trades convenience for control, and we will cost both options honestly.

What if the accuracy is not good enough?
Then we say so. Some use cases are not ready, and a short evaluation costs far less than discovering that after a build.