AI development, engineered for production.

It designs, builds and runs AI systems inside a business: LLM applications, assistants over company records, document intelligence, predictive models and AI features in a product. Most of the work sits around the model: connecting your systems, grading the output on your own cases, guarding against misuse, and keeping quality and cost visible in production. DigyAi does all of it, in your environment, and the code is yours.

Ask each firm five questions and expect written answers: how will you grade quality on our own cases, who signs the release, what will each task cost at our volume, what do we own at handover, and how do you defend against prompt injection. Then ask to see an evaluation report and an architecture from work they have shipped. This page answers all five for DigyAi.

Buy when the task is generic and the tool's data and security terms fit. Build when the work depends on your records, your systems or your rules, or when the tool cannot be graded on your cases. Discovery settles it in writing, and recommends buying when buying is the better choice.

Pilots stall on integration, unproven quality, unmodeled cost and missing controls, and rarely on the model itself. We build the connectors first, grade the system on your cases, model the cost per task, and put guardrails and tracing in place before launch. Then it passes a written gate that your owners sign. A pilot that has already stalled can be picked up: discovery finds where it stopped and recommends whether to rescue or restart.

It is the engineering of software around a large language model so that it does a business task reliably: prompts and typed tools, retrieval over your data, integrations with your systems, guardrails, evaluation and monitoring. The model is one component. The application is what makes its output accurate, safe and affordable at your volume.

Retrieval-augmented generation (RAG) fits when answers depend on information that is private, recent or changes often: the model reads your records at question time and cites them. Fine-tuning fits when the task is narrow and high in volume and a smaller model can learn it, which cuts cost and latency. Many systems use both, and your evaluation set decides.

Whichever wins on your evaluation set, step by step. Frontier models through enterprise endpoints take the hardest reasoning, small or open-weight models take routine steps, and open weights run inside your environment when data cannot leave it. Model choice is configuration, so moving to a better model is a config change followed by a run of your graded cases.

Yes, and the integration comes first, before any model work. Systems with a usable API get a typed connector with retries and an audit row. Older systems get a thin integration service that we build and document, reading from the database or the exports they already produce. Where a system can be read safely but not written to, the AI prepares the update and a person posts it.

Against a graded set built from your own historical cases, with pass and fail thresholds agreed in writing. We combine code checks, model graders calibrated against your experts, and human review; we test for prompt injection and data leakage; and the whole suite runs again on every change to a prompt, a model or a retrieval setting. After launch, samples of live answers are scored against the same thresholds.

No. Models are called through enterprise endpoints configured for zero data retention, or run as open weights inside your environment, and nothing is used for training. Prompts, traces and evaluation data stay in your storage under your access controls, and an NDA and a DPA are signed before discovery starts.

By design first, then by testing. Retrieval enforces each user's permissions, no component holds private data, untrusted content and a way to send data out at the same time, and every output is validated before it reaches a system or a person. Every release is then attacked against the OWASP Top 10 for LLM Applications, and the results go to your security team.

It depends on what the system decides and for whom. Most business assistants carry transparency duties, while systems used in areas such as hiring, credit or insurance can be high-risk, with heavier obligations. We classify each system during discovery and deliver the records your compliance team needs: data flows, evaluation results, human oversight and logging.

Four things drive the build cost: how reachable and clean your data is, how many systems the AI reads from and writes to, how accurate it must be before launch, and your security and compliance review. Discovery ends in a written proposal for each phase, approved before it starts. The running cost is inference plus hosting, modeled per task before launch and billed to your own accounts, so there is no margin on your usage.

Discovery takes one to two weeks and ends in a dated plan for your case. Three things set the release date: how reachable your data is, whether enough past cases exist to grade the system against, and how long your security review runs. From then on you see working software every two weeks.

All of it: the code, prompts, evaluation sets, fine-tuned weights, indexes, dashboards and runbook, in your repositories and your environment from the first commit. Your team can run and extend the system without us, and we stay on to run it only if you choose.