Your own private AI, set up and run for you, so your data stays with you.
For firms that cannot send documents to outside AI services. We install an open AI model on your own servers or private cloud, so staff use AI for drafting, summarising and questions while nothing leaves your network.
For businesses whose AI needs to write or classify in their own way. We build a repeatable process that turns your example data into a custom-trained model, tested against held-back examples before each new version goes live.
For businesses with lots of documents staff or customers search through. We build an AI that reads your documents and answers questions with citations to the exact source, tested against your own real questions.
We build a versioned evaluation suite that scores every model or prompt change against a golden dataset before it ships, catching regressions automatically. Results are tracked over time so you can see quality trending across releases.
We build an inline layer that detects and redacts names, emails, card numbers, health identifiers, and custom entities before text reaches any model or log. Redactions are reversible via a secure vault when authorized, so downstream systems still work.
For businesses with AI features that want proof one version beats another. We build experiments that split live traffic between prompt or model versions and measure real business results.
For app makers whose AI must work fast, offline or privately. We shrink a model and install it inside your phone app, browser app or device, so it runs without a server.
For teams whose AI lacks examples of rare cases. We build a pipeline that generates realistic made-up examples, checks them against your real data and records what was generated and why.
We build a validation layer that enforces schema, blocks unsafe or off-policy content, and checks factual grounding on every model response before it reaches a user. Failing outputs are auto-corrected or retried instead of shipped.
We build a feedback loop that captures user thumbs, corrections, and choices and turns them into DPO/RLHF training signal to steadily align the model to your users. Each cycle is measured against a held-out set to confirm real improvement.
We set up autoscaling GPU infrastructure that spins capacity up under load and down when idle, with queueing and priority classes. Jobs and inference share the pool efficiently so you stop paying for idle accelerators.
For businesses running their own AI models on costly servers. We tune your model serving so the same hardware handles more requests faster, and we measure before and after.
We deploy a grounding verifier that checks each generated claim against its source passages and flags or blocks unsupported statements. It scores faithfulness in real time and can force a regeneration when the answer drifts.
We distill a large model's behavior into a small, cheap one that runs at a fraction of the cost while keeping most of the quality on your task. We generate teacher outputs, train the student, and prove the quality gap is acceptable.
We embed an AI feature — summarization, drafting, search, or classification — directly into your existing app with proper auth, rate limits, error states, and analytics. It ships as production code in your stack, not a demo.
We build a defense layer that detects and neutralizes prompt-injection and jailbreak attempts hidden in user input or retrieved documents. It isolates untrusted content, strips instructions, and blocks tool misuse before harm occurs.
For businesses launching or growing AI features. We simulate many users at once against your AI and show how fast it responds, where it breaks and what capacity you need for growth.
We deploy monitoring that watches input distribution and output quality over time and alerts when the model starts drifting from its training baseline. It separates data drift from concept drift so you know whether to retrain or rethink.
For teams training and running their own models. We build a feature store so the same data definitions feed both training and live predictions, ending mismatches that quietly break models.
We build a pipeline that runs models over millions of records efficiently with checkpointing, retries, and cost controls. Large scoring jobs run overnight and resume cleanly if interrupted.
We set up progressive model rollouts that send a small slice of traffic to a new version, watch quality and error metrics, and auto-promote or auto-rollback based on the numbers. Bad models never reach all your users.
For businesses whose AI search misses because of specialist terms. We train the search model on your terms and examples so it finds the right documents, and measure the improvement against the standard model.
We build a memory layer that stores, summarizes, and retrieves conversation history so your assistant remembers users across sessions without blowing the context window. It balances recall with cost through tiered summarization.
For software companies adding AI features for many business customers. We build one backend that keeps each customer's data strictly separate, meters their usage and lets you set limits and models per plan.