AI
AI development services, built to survive production.
TechFabric builds AI systems that run in production: agents with governed data access, workflows that survive a restart, and the evaluation harness that tells you whether any of it is working. We run our own go-to-market on the same stack, so the patterns we recommend are ones we operate.
Most AI work stalls in the same place. The demo is convincing, and then somebody asks what happens when the model times out halfway through a refund, or who approved the thing it did at two in the morning, or whether last month's version was better. We build the system around the model.
- Durable
- A run survives a restart, a deploy and a provider outage, and picks up where it stopped rather than starting again
- Governed
- An agent runs under its own service principal and inherits Unity Catalog grants, so it cannot read what its caller could not
- Measured
- A fixed set of cases with known answers, scored on every change, so quality is a number rather than whoever spoke last
Two ways to start, both fixed fee
Fixed length, fixed price, and a written deliverable whether or not you do anything else with us. Which one you want depends on whether the question is what to build or what you are already paying for.
Two weeks
AI Readiness Assessment
Most AI programmes are not blocked by the model. They are blocked by data nobody trusts, permissions nobody can explain, and a platform where getting an agent to production means six approvals and a service account somebody created in 2023. Two weeks tells you which of those you have.
Book a readiness assessmentTwo weeks
AI Tools Assessment
Three years of buying has left most companies with Copilot seats, a ChatGPT tenancy, a coding assistant per team, and four pilots nobody measured. Somebody in finance has started asking what it bought. Usually nobody can answer, because the seats and the outcomes were never on the same page.
Book a tools assessmentAnd four ways the work continues
Agentic AI development
Engineering leaders whose agent pilot cannot get past review
AI workflow automation
Operations and engineering leaders with a process that half-completes
AI enablement
Engineering leaders whose team needs to own this, not outsource it
AI transformation
Executives with a mandate, a budget, and fifteen pilots
What we build on
The accelerators are free, open source, and run in your workspace
These are not products for sale. They exist because we needed them on client work, they are what makes the first agent take weeks instead of quarters, and they deploy into your own account rather than ours.
Fabric Platform
A portable runtime for ontology-based, agent-native business applications.
Fabric Harness
A TypeScript framework for durable, deployable autonomous agents.
Fabric Tower
Mission control for AI agent squads. Where people watch, steer, and approve the work.
Fabric Experiments
A Databricks adoption accelerator for quality engineering, experimentation, and governed delivery.
FAQ
AI development: common questions
What are AI development services?
Building the system around the model rather than the model itself. In practice that is retrieval and context, permissions so an agent cannot read what its caller could not, durable execution so a long run survives a restart, human approval where a decision needs one, and evaluation so you can tell whether a change made things better. Most teams can get a prototype working. The engineering is in everything after.
Do you build on a specific model or provider?
No. Model choice changes every few months and a system tied to one provider is a system you rebuild. Our agent framework routes through a provider registry, and on Databricks that is Unity AI Gateway. We will have an opinion about which model fits your workload, and it is not an opinion worth hard-coding.
We have a pilot that works in a demo and nowhere else. Can you help?
That is the most common way this starts. Usually the demo is fine and the gap is durability, permissions, cost, or the fact that nobody can measure whether it is any good. We work out which of those it is before proposing anything, because the four have very different answers.
Do we have to be on Databricks?
No. Databricks is where we go deepest and where the governance story is strongest, because an agent can inherit Unity Catalog permissions instead of routing around them. We also build on Azure, AWS, Google Cloud and Cloudflare, and plenty of this work starts before a lakehouse exists.
How do you price AI work?
The assessments are fixed fee and fixed length, so you know the cost before the first meeting. Build work is scoped after one of those, or after a conversation if you already know what you need. We would rather write a scope we can defend than quote a number against a brief nobody has read properly.