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CITRIS AI Engineering

Agents that work alongside what you already run.

Custom agents, frameworks and accelerators that sit beside your existing applications and databases. Built to your boundary, evaluated before they ship, governed from the first commit.

Talk to us about custom AI See the proof of work →

CITRIS AI Engineering

We build agentic AI that works alongside what you already run.

Custom agents, frameworks and accelerators that sit beside your existing applications and databases — never a rip-and-replace.

Most organisations do not need another platform. They need their current systems to get smarter. We design and build agentic AI with the same engineering discipline that runs inside Artec Neo, connected to what you already own.

  • Ingest

    Data onboarding agents

    Point them at any legacy database, spreadsheet estate or API. Schema inferred, mappings proposed, humans approve, deterministic pipelines execute.

  • Extract

    Document-to-record extraction

    O&M manuals, contracts and certificates become structured, evidenced data — with per-field confidence and a citation back to the source page.

  • Decompose

    Obligation intelligence

    Contracts and SLAs decomposed into dated, owned, trackable obligations, so nothing lives only in a PDF nobody has opened since signature.

  • Query

    Ask-your-systems

    Natural-language answers over your own data, read-only by construction. The agent cannot write, so it cannot break anything.

  • Build

    Custom agent frameworks

    Designed around your workflows and deployed in your cloud — AWS Bedrock, Azure AI Foundry, or your own endpoints. Your data never leaves your boundary.

The trust frame — reused from the product, because it is the moat

  • Propose / dispose

    Agents draft. Deterministic code executes. Humans approve. The model is never the last thing between an idea and your database.

  • Your model, your keys, your cloud

    Bring your own model on AWS or Azure. We build to your boundary rather than asking you to move inside ours.

  • Evaluated, not vibes

    Every agent ships with a measured accuracy number against a held-out set, and a regression gate that fails the build when it drops.

  • Governed by default

    Audit trails, cost caps, tool allow-lists and PII redaction. The control plane is part of the build, not a phase-two extra.

Artec Neo is our proof of work. Every discipline we sell — evaluation gates, audit chains, human-in-the-loop, cost governance — runs in production inside our own platform first.

How we engage

Three steps, and permission to stop after the first

The most valuable thing a two-week sprint can produce is sometimes the finding that the idea does not work. We would rather tell you that in a fortnight than bill you for a year of discovering it slowly.

  1. 01

    Feasibility sprint

    2 weeks

    We take one real workflow and one real dataset, and establish whether an agent can do the job to a measurable standard. You get an accuracy number, a cost-per-run figure, and an honest recommendation — including “don’t”.

    You get Working prototype, evaluation set, go/no-go with numbers

  2. 02

    Build and evaluate

    6–12 weeks

    The agent is built against your systems, with the control plane — audit, cost caps, tool allow-lists, PII handling — in from the first commit rather than retrofitted before go-live.

    You get Deployed agent in your cloud, regression gate in your CI

  3. 03

    Operate or hand over

    Your call

    We run it, or we teach your team to. Either way the evaluation harness stays, so drift shows up as a failing build rather than as a complaint from a user six months later.

    You get Runbooks, eval maintenance, or a managed service

Straight answers

Four things you will hear from other vendors that you will not hear from us

  • “The agent will handle it end to end.”

    Agents are excellent at proposing and terrible at being accountable. We put deterministic code between the model and anything that writes.

  • “We’ll fine-tune on your data.”

    Usually the expensive answer to a retrieval problem. We reach for fine-tuning last, and only when an evaluation shows retrieval genuinely cannot get there.

  • “It works — look at these examples.”

    Demos are chosen; evaluations are not. We hold out a test set before we start, and we report the number it produces, including when it disappoints.

  • “Send us your data and we’ll take care of it.”

    We build inside your boundary, on your keys, in your cloud. Data leaving your control should be a decision you made, not a side effect of a vendor’s architecture.

Next step

Bring us the workflow that hurts.

The one where somebody re-keys data between two systems, or reads a hundred PDFs to answer one question. That is where an agent earns its keep.

Book a demo Talk to us about custom AI

No sales sequence, no gated PDF. A working session with the people who built it.