Your model is 5% of the system
Shipped to production since before it was cool.
Applied AI means the version that survives contact with real users - is the engineering between a model that works and a system people can rely on: the data pipeline feeding it, the evaluation that proves it still behaves, the latency and cost budgets it has to live inside, and the monitoring that tells you when it has quietly started being wrong. The model itself is usually the smallest part.
We build that part. Sometimes with a large language model, sometimes with a forecasting model that is thirty years old and perfectly adequate, and sometimes by telling you the problem is a database query wearing a very convincing hat.
Everyone can demo. We build the version that survives real traffic, real data and real consequences.
ML engineers, data engineers, and the people who get paged when a model misbehaves at three in the morning. We've opened enough stalled projects to know which corner usually got cut first and we can normally tell you which one in a week, without a strategy phase.
The full AI menu. Order anything
Describe the use case. We'll tell you whether it needs a model at all.
The list runs the way a real system takes shape - decide whether to build it, get the data ready, build the thing, prove it still works, then keep it working. Most pilots that stall skipped the fourth step. Any item on the list can be a first engagement.
Use-case triage - build it, buy it, or drop it
Data readiness and the feature pipelines underneath
RAG over your own documents, with the vector database to serve it
LLM integration into existing products and internal workflows
Conversational AI, copilots and support assistants
AI agents and multi-agent orchestration, with a defined stopping condition
Predictive analytics, forecasting and demand planning
Recommendation and ranking engines
Anomaly and fraud detection on live traffic
Real-time decisioning at production traffic volumes
Computer vision for inspection, counting and document capture
Generative creative at scale
Fine-tuning and domain adaptation, when prompting has genuinely run out
Evaluation suites and guardrails - how you know it still works
MLOps, drift monitoring and the governance the EU AI Act will ask for
The good stuff is always in the details
The model is the easy part
We ship an evaluation suite with it
Latency and cost are design decisions, not surprises
Trained on what, exactly?
The day your model gets deprecated
Show us where the pilot stopped
to set launch date
Who this is for
- A demo the board already saw
- A model nobody has deployed
- Documents nobody can search
- Support volume growing faster than headcount
- Forecasts made in a spreadsheet
- Decisions made by eye, at scale
- A pilot that stalled in month four
- Token bills nobody can explain
- An AI feature with no way to test it
- A regulator who will ask how it decided
Three of those and it's not an experiment any more. It's a system without an owner.
Everything the demo didn't answer
Rarely because the model is bad. They fail because nobody agreed in advance what “working” means, so the pilot has no finish line; because the data that made the demo look good doesn’t exist in that form in production; because latency or token cost turns out to be unaffordable at real volume; or because no team was ever named to own the thing after launch. All four are decisions, not technical limitations, and all four are cheaper to fix before the build than after.
There are two costs and people usually budget only the first. The build is a few weeks to a few months of a small senior team. The running cost – inference, retrieval, monitoring, and the engineering time to keep quality steady – continues for as long as the feature exists, and at real volume it frequently exceeds the build. We model both before you commit, because a feature that is affordable to make and unaffordable to run is a worse outcome than not building it.
Start with retrieval. RAG suits the common case: answers that must be grounded in your own documents and change as those documents change, and it is cheaper, faster to adjust and far easier to audit, because you can see which source produced an answer. Fine-tuning earns its place when you need a consistent format, tone or domain vocabulary that prompting cannot hold, and it is usually a refinement after retrieval works, not an alternative to it.
With a fixed test set of real inputs, expected behaviours and a scoring method agreed with you, run as a regression suite every time anything changes: the prompt, the model version, the retrieval index. The point is not a perfect score; it is that when quality moves, you know within minutes and you know what moved it. Without this, teams change prompts by feeling and discover regressions through customer complaints.
Almost certainly not. Training a model from scratch is justified by proprietary data at a scale very few companies have, and it commits you to maintaining something that commercial models will overtake. The decision that actually matters is which parts of your system are allowed to depend on an external provider, and what happens when that provider deprecates a version, which we design for explicitly.
MLOps is the practice of deploying, versioning, monitoring and rolling back models the way mature teams already treat ordinary software. You need it the moment a model influences a decision a customer or a regulator could question. Before that point it is overhead; after it, its absence is why nobody can explain what changed last week.
Monitor the inputs, not only the outputs. Drift usually shows up in the data distribution before it shows up in a metric anyone is watching, so we alert on both, re-run the evaluation suite on a schedule, and keep the previous model version deployable so rollback is a decision rather than a project. Degradation is normal and manageable. Being surprised by it is the failure.
Your AI pilot deserves a production date
You don't need another proof of concept.