# AI consulting and development, 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 delivery on the same stack, so the patterns we recommend are ones we operate. Canonical: https://www.techfabric.com/ai --- 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. ## Pages - [AI Readiness Assessment](https://www.techfabric.com/ai/readiness): A two-week fixed-fee assessment of whether your data, permissions, platform and team can support the AI systems you want to build. You get a written readiness scorecard, the specific blockers ranked by what they cost you, and a thirty, sixty and ninety day order to clear them. - [AI Tools Assessment](https://www.techfabric.com/ai/tools-assessment): A two-week fixed-fee audit of the AI tools your company is already paying for: seats bought against seats used, where four products do the same job, what the licences renew at, and which of them changed how the work gets done. You get a spend and adoption picture, and a consolidation plan. - [Agentic AI development](https://www.techfabric.com/ai/agents): We build production AI agents: durable across restarts, bounded by the same catalogue permissions as the people they act for, stopping for human approval where a decision needs one, and measured by an evaluation harness so you can tell whether a change helped. Our agent framework is open source and deploys into your own workspace. - [AI workflow automation](https://www.techfabric.com/ai/workflows): We automate long-running business processes with AI in the loop: document intake, approvals, exception handling, multi-step operations that touch several systems. Built on durable execution, so a workflow survives restarts, retries and partial failure, and every run leaves a history you can audit. - [AI enablement](https://www.techfabric.com/ai/enablement): AI enablement is our engineers working inside your team on real production work, so your people can build and run the next system without us. Not a training course. A first system shipped together, with the patterns, the review standards and the evaluation harness left behind in your repository. - [AI transformation](https://www.techfabric.com/ai/transformation): AI transformation work at TechFabric starts by finding which parts of the business would actually change if a system ran there, and then building those. We sequence a programme around what can be measured, retire the pilots that were never going to ship, and put governance under the ones that stay. ## 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.