Put AI to work inside the product you already have. We add assistant access, retrieval, and automation on top of your Symfony domain, with evaluation, cost control, and security boundaries that make it safe to ship.
Every competitor announces an AI feature and the board asks why you have not. The proofs of concept demo well and then stall, because nobody can answer what they cost per request, what they do with customer data, or what happens when the model is wrong.
We start with the workflow, not the model. We find where AI removes real effort in drafting, classification, retrieval, and triage, and where it adds risk for no gain, so you invest in the use cases that pay back.
We design the integration as part of your domain instead of a bolt-on. Retrieval over your own data, a clear boundary for what the model can read and do, and an evaluation harness so quality is measured rather than felt.
We build it into your Symfony application, reusing the repositories, value objects, and security you already trust. MCP servers where assistants need access, queued workers for heavy calls, and observability from the first request.
A ranked shortlist of use cases with value, feasibility, and data sensitivity for each, plus a build-versus-buy recommendation and a cost-per-request estimate. The document that turns board pressure into a fundable plan.
AssessmentA Model Context Protocol server built on your Symfony domain, exposing a small and audited set of tools with authorisation per user, input validation, and logging, so assistants can act on your systems without a blank cheque.
BuildAn evaluation harness with a versioned test set and metrics, prompt-injection and authorisation boundaries, plus cost and rate controls, so you can change prompts and models on evidence instead of vibes.
QualityYour engineers paired through the integration, with the patterns, the evaluation workflow, and the security checklist documented as the reference they use for the next AI feature without us.
EnablementMost teams should start with a hosted model API and only consider self-hosting when data residency, cost at scale, or latency forces it. We make that call with you on numbers rather than fashion, and design the integration so the model is a swappable dependency, not a foundation.
That is the first thing we pin down. We map every input a model would see, decide what may leave your infrastructure and what must not, and prefer retrieval over your own data to copying it into a vendor you cannot audit. The data boundary is a design input, not an afterthought.
We build an evaluation set before launch, a versioned collection of real inputs with expected outcomes, and measure every prompt or model change against it. You stop arguing about whether the output feels better and start seeing whether it scored better.
Rarely. The highest-value work is usually quieter, in classification, retrieval, drafting, triage, and giving an assistant safe access to your systems through an MCP server. A chat window is one interface, and often not the one that pays back first.
Yes, that is the point. We build AI features inside your app, reusing your repositories, value objects, and security, so the integration shares the code you already trust instead of drifting as a separate service that nobody maintains.