AI that still works after the demo.
Saintrac builds AI agents and integrations for actual operations: retrieval, workflow automation, and multi-agent systems that have to hold up under real use, not just in a controlled demo.
The proof is what we run ourselves. Saintrac operates a multi-agent layer across three runtimes: cloud, serverless, and on our own hardware. Agents route work, retrieve from versioned markdown memory, and handle operational tasks that used to require a human in the loop. That infrastructure is production for us, which is how we learned what breaks and what does not.
If an integration pattern does not survive on our own systems, it does not go into client work.
Who it's for
This page is for companies that see a place where an agent or AI step belongs in an operational workflow, and need it built so it still works a month after launch, not a demo that breaks under real data.
Typical fits:
- A business where agents could route, triage, or summarize work that currently requires a person reading every request.
- A company building semantic search into a product, where retrieval has to work on real documents and edge cases, not the clean examples from the pitch.
- An operation where AI could automate a step in a workflow, but only if it is designed to fail gracefully and hand off to a human when it cannot proceed on its own.
If the workflow is web, portal, or software without an AI component, see Custom software or Web design.
The business problem
Most companies do not fail at running a demo. They fail at running it in production.
A prototype that routes ten clean requests breaks on the eleventh, the one with unexpected formatting or missing context. Retrieval that works on curated documents returns nothing useful when the real corpus includes scan artifacts, inconsistent structure, or a vocabulary the embedding model never saw. The agent that looked intelligent in a controlled test becomes a support burden when it confidently invents answers under ambiguity.
The demo proves the idea works. Production is where you learn whether it holds up.
What Saintrac delivers
AI integration or agent infrastructure, scoped in writing, with a fixed price and a real timeline.
What you get:
- Agents designed for the edge cases: what happens when input is malformed, when retrieval finds nothing useful, when the model refuses or produces nonsense. Graceful failure paths, not just the happy path.
- Multi-agent systems when the workflow legitimately calls for specialisation, orchestration, and handoff between agents. Not as a way to demo complexity.
- Semantic search and retrieval wired into the actual document corpus, with chunking, embeddings, and ranking tuned to the vocabulary and structure of your real data.
- Integration with the tools your business already uses: CRM, support desk, document store, or workflow software. The agent lives where the work happens, not in a separate demo environment.
- Handover: your infrastructure, your model accounts, your keys, plus a walkthrough of how it works and how to debug it when it does not.
AI integration does not include a week-one staging link. The timeline is scoped after the first call. No published week range.
How an engagement runs
01
Get specific
A call, then a written scope with a fixed price and a clear timeline. If we are the wrong fit, you hear it here.
02
Build in the open
A live staging link from early in the build. You see progress as it happens instead of waiting for a late reveal.
03
Hand over the keys
Your accounts, your repo, your CMS, your domain, plus a walkthrough so your team can run it.
04
Stay reachable
Ongoing support if you want it, never as the price of editing your own site.
Proof of practice
Saintrac does not have a published client AI case study. The proof is what we run ourselves, and what we are building.
Production agents for Saintrac:
A multi-agent system deployed across three runtimes (cloud, serverless, and local hardware) handling operational work. Versioned markdown memory, tool use, and routing between agents specialised for different tasks. This infrastructure is production for our own operations, which is how we learned what breaks.
Own products in development:
- Tuni: semantic layer for multi-source analytics, including semantic search over structured and unstructured data. In development.
All products are in development. Tuni's semantic search is being built to the same standards we hold for client work: it has to retrieve useful results from real data, not just clean examples.
Related client work (not AI cases):
- Egort & Partners: Client portal for a financial practice. Webflow, Xano, Wized. See case study.
- Nadalsa: Webflow site and ongoing marketing operations for a freight company. See case study.