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.
For firms handling confidential documents who want AI help but cannot let anyone, even the server host, read the data. We set up an AI model inside a sealed, encrypted compute area and give you proof your data stayed inside it.
For businesses buried in emails, documents and notes that mention part numbers, product codes or other details staff copy out by hand. We train an AI to pick those details out automatically and drop them into a spreadsheet or system.
We build a classifier that routes incoming messages, tickets, or queries to the right intent and team with confidence scores and an unknown-intent bucket. It retrains from corrections so accuracy climbs over time.
For brands and agencies that need lots of product and campaign images in a consistent style. We set up a private image generator trained on your products and look, so your team creates on-brand images without paying per picture.
We add watermarking and content provenance to your AI-generated text or images so outputs can be detected and traced back to your system. This supports AI-content labelling, brand protection, and misuse investigation.
We build an in-product feedback capture and labeling platform that turns user signals and reviewer decisions into structured training data. It closes the loop from production output to the next model improvement.
For teams running an AI assistant over their documents who cannot tell if a change makes answers better or worse. We build a test bench that scores how well your system finds the right documents, and reruns after every change.
For companies whose AI models get stale as customers and products change. We build a pipeline that retrains your model when new data arrives, tests it, and only puts it live if it does at least as well as the old one.
For businesses that want customers to speak to an AI on the phone or in their app and get a natural back-and-forth. We build the voice engine that listens, answers from your own information and talks back, letting callers interrupt like a real conversation.
For shops and booking sites whose recommendations only work for regular customers. We add a layer that suggests good products to brand-new visitors and gives brand-new products a fair chance, using what items are like and the visit context.
For software companies whose AI must produce answers that can be checked automatically, like database queries or code that must pass tests. We train your model against your own checker so it learns from what is actually right.
For companies whose AI bills keep growing without knowing who uses what. We put one gateway in front of all your AI traffic that tags each request with the team, sends costs to your finance codes, and caches repeated requests to save money.
For software companies whose busiest AI feature is slow because of the code around the model, not the model itself. We rewrite that one slow path in a fast systems language, keeping the same interface so nothing else in your product changes.
For companies running an AI agent as a simple script that starts over whenever something fails. We move it onto a durable workflow engine so every step is saved, a crash resumes where it stopped, and actions like payments or emails never happen twice.
For organisations with a very large library of their own documents, like technical manuals or research archives. We continue training an open model on that library so it truly understands your field's words, then fine-tune it for your tasks.
For companies whose AI makes decisions customers may question later. We record each decision with its input, model version and output in a log that cannot be edited, so you can show exactly what happened months later.
For companies running AI models on small computers at many sites, like cameras in stores or sensors in plants. We set up central management that rolls out model updates safely, watches every site's health and accuracy, and rolls back if something goes wrong.
For plants where the same pump or motor has a different ID in every monitoring system. We match them into one asset record and build a shared data store, so every model and report sees all data about each machine together.
For enzyme engineering teams that can only make a fraction of the designs they dream up. We tune a protein AI on your own assay results so it scores thousands of candidate sequences a day before any go to the lab.
For engineering teams waiting hours for each simulation run. We train a fast AI stand-in on your past simulations and real sensor data so engineers get near-instant answers inside their design tools.
For analytical chemists who spend hours working out structures from NMR or mass spectra. The AI, tuned on your confirmed structures, suggests ranked candidate structures from your spectroscopy software.
For engineers whose simulations drift from what the plant or lab really measures. We find the unknown physical parameters that make your simulator match real measurements, and repeat it automatically as new data arrives.
For software teams whose tests pass but still miss bugs. We find the code paths your tests don't really protect, write new tests for exactly those, and add a check that stops weaker test coverage from being merged.
For companies running a critical system whose authors are long gone. We watch what the code really does under test traffic, build a searchable map of its behaviour and write tests that lock it in before anyone changes it.
For software teams whose senior engineers keep writing the same review comments. We train a review bot on your own past reviews, including which comments led to changes and which merges were later reverted, so it gives every new pull request a first review in your team's style.
For software companies paying for AI on every user request. We measure which AI model is good enough for each kind of request using your own past answers, then route each request to the cheapest one that passes.
For companies whose AI assistant answers from their documents but often gets it wrong. We find out whether it is fetching the wrong documents or misreading the right ones, fix the search, and rebuild your index.
For products where users keep asking similar questions. We store good AI answers and reuse them when a new question means the same thing, so you stop paying again for answers you already have.
For companies with lots of AI usage logs and a task general models keep getting wrong. We turn your past requests and your team's corrections into a training set, train your own tuned model and prove whether it beats the standard one.
For companies with AI features and a support inbox full of past mistakes. We turn closed tickets and your team's corrections into tests your AI must pass, run them on every change and report what fails.
For companies running several AI features and losing track of what they cost and how well they work. Every request is traced from question to outcome, so you see spend and quality side by side per feature.
For companies whose cloud bill keeps growing. We measure what your services really use over a full busy cycle, resize them, add sensible auto-scaling and cheaper spare capacity, and roll back automatically if anything slows.
Your team asks questions like 'which stores sold fewer umbrellas this month' in chat and gets an answer pulled from your own database, with the working shown.
we build and deploy the connection that lets AI assistants sign in to your product and use it on your customers' behalf, with limits you set.
When the AI model your product uses is being shut down, we move you to a replacement and test on your real requests that it works as well before switching.
We take the prediction model someone built in a notebook, get it working again, and run it as a proper service your systems can use, with checks that it still works.
Every AI model or dataset your team downloads is automatically checked for hidden harmful code and licence problems before it can be used.
We collect sensor recordings from your device, train a tiny model that recognises the events you care about, and fit it onto your device's chip.
Before an AI model trained on your private data goes live, we test whether it can be made to repeat that data, and stop it from shipping if it can.
We group everything users ask your AI feature into topics and show which kinds of requests it handles badly, so you know what to fix next.
Before each update of your chatbot goes live, it runs hundreds of realistic practice conversations against it and show you where it failed.
We build a model that sorts your records into your own categories, using rules from your experts instead of months of manual tagging.
We add a confidence check to your existing AI model so it only acts automatically when it's reliably sure, and sends the rest to your team.
We find the specific kinds of cases where your AI model makes mistakes, name them, and keep checking them after every update.
We add a feature to your software where each of your customers can train their own version of your AI on their data, tested before it goes live.
We package your AI product so your customers can install and update it on their own servers or cloud accounts, with licence keys.
We train a code-suggestion model on your own codebase and run it on your servers, so your developers get suggestions that fit your code without it leaving your company.
We put a checkpoint between AI agents and your business systems so they can only do what you allow, and anything risky waits for a person to approve.