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.
We deploy a service that enforces per-user, per-team, and per-plan token and request limits with graceful degradation and clear quota feedback. It protects your budget and prevents any one client from starving the rest.
We build and deploy a moderation classifier tuned to your policy that flags toxic, unsafe, or off-policy content in text or images at scale. It ships with a review queue and continuous retraining from moderator decisions.
We deploy an ASR pipeline with speaker diarization, timestamps, and domain vocabulary tuning that transcribes calls and meetings recorded with participants' notice accurately. It runs on your infrastructure so recordings never leave your control.
For online stores and content sites that show everyone the same items. We build a recommendation engine that learns from browsing and buying to suggest the right products to each visitor, with your business rules on top.
For businesses that want to know when something odd happens — a sales drop, a payment spike, a system error surge. We deploy a model that learns what normal looks like and alerts you to real anomalies, not noise.
We build an adversarial test suite that automatically probes your AI for jailbreaks, data leakage, bias, and policy violations, and reruns on every release. Findings come with severity, reproductions, and remediation guidance.
For businesses whose questions need linking facts across many records. We combine a map of how your people, companies, products and documents connect with document search, so the AI answers multi-step questions.
We build retrieval and generation over both images and text so users can query across diagrams, screenshots, product photos, and documents together. Images are embedded, retrieved, and reasoned about alongside their surrounding text.
For companies offering custom AI per customer or task. We set up serving where one base model swaps in many small custom add-ons, so each customer gets their own tuned version without a dedicated server.
We shrink your model through pruning, quantization, and structured compression to fit tighter memory and cost budgets while measuring the quality tradeoff at each step. You get the smallest model that still meets your quality bar.
For sites that must keep data fully offline — factories, labs, private archives. We deliver a complete AI system that runs with no internet at all: the model, document search and a chat screen, updated only by signed offline packages.
For software teams connecting several apps to their AI backend. We build a typed code library with retries, streaming, logins and error handling built in, plus documentation and examples in the languages your team uses.
For businesses whose AI search outgrew its demo setup. We move your search database to a production setup that handles growth and failures, switching over without downtime.
We build a harness that programmatically searches and optimizes prompts against your eval set, treating prompt engineering as a measurable optimization problem. It converges on prompts that outperform hand-written ones.
For teams that need to grade AI output at scale. We build an AI judge that scores answers on your rubric and check it against human ratings so you can trust its scores.
For businesses whose chatbot frustrates customers when it gets stuck. We build a chatbot that answers from your content and passes the conversation, with full context, to a person when it is unsure or the customer asks.
We construct a rigorously curated golden test set for your AI task with balanced coverage, expert labels, and adjudicated edge cases. It becomes the ground truth every eval and regression check runs against.
For businesses whose AI search goes stale or costs a fortune to rebuild every night. We build a pipeline that updates only what changed, with checks that search stays consistent.
We instrument your AI infrastructure to attribute GPU and token spend to teams, features, and customers, and surface waste like idle GPUs and oversized models. You get a live cost picture and concrete savings actions.
For tech teams that already run their software on Kubernetes and now have their own AI models to serve. We set up model serving that scales up when traffic rises, scales down when it is quiet, and rolls out new model versions safely.
We build a layer that constrains model output to a strict JSON schema or grammar so downstream code never chokes on malformed responses. Invalid outputs are constrained at generation time or repaired and revalidated.
We build a pipeline that finds and removes near-duplicate content by meaning, not just exact text, cleaning training sets and knowledge bases of redundancy. It clusters similar items and keeps the best representative of each.
We train a custom cross-encoder reranker on your click and relevance data so ranking reflects what your users actually find useful. We measure NDCG gains over generic rerankers before shipping.
We deploy a gateway that unifies multiple model providers behind one API with automatic failover, load balancing, and provider-agnostic keys. If one provider degrades or rate-limits you, traffic shifts seamlessly.