guide
How to hire an ML engineering agency (2026)
What to ask, what to look for in a proposal, and the questions that separate a real ML team from an LLM wrapper shop.
ReadWhat is blocking you
Four reasons the AI feature is still on the roadmap.
Every customer asks for AI in the roadmap call
Your sales team keeps hearing the same question and keeps giving the same answer about the roadmap. Meanwhile a competitor ships a scoring feature that is thin but demoable, and the comparison sheet starts going against you.
Hiring an ML team is a twelve-month decision
One senior ML engineer is a long search and a permanent line on the payroll for what might be two features. And a single hire has no bench - the moment they take a month off, the model has no owner.
A wrapper around an LLM will not survive procurement
Shipping a prompt as a feature works until a customer's security or data team asks how the number is derived, where the data goes, and what happens when the provider changes the model. That review is where thin AI features die.
Your customers' data schemas are all different
Every deployment is slightly bespoke. A generic model trained on one customer's data does not transfer, and building per-customer models by hand does not scale past a handful of accounts.
What we build for you
Six pieces, delivered as your product.
The scoring service, under your brand
A deployable service that takes your schema in and returns scored records out. Your name on it, your docs, your support. Under NDA we do not appear anywhere in the deliverable or in our own portfolio.
Per-tenant model training
One pipeline, many tenants. Each customer's model trains on their own data with your shared feature definitions, so accuracy is per-account but the operational cost is one system, not N systems.
Explainability built into the response
Every score comes back with the features that drove it. This is what your customer's analyst needs to trust the number, and what your customer's compliance reviewer needs to sign it off.
Schema adapter layer
We map each tenant's data model to a shared feature card once, at onboarding. Adding a customer becomes a mapping exercise rather than a modelling project.
Retraining and drift monitoring
Scheduled retraining per tenant, distribution monitoring on inputs and outputs, and alerting when a model starts drifting. Delivered as runnable infrastructure with a documented runbook.
Handover so you are not dependent on us
Full source, feature pipeline, training scripts, evaluation procedure, and calibration step documented. You can take it in-house whenever you want. The retainer is optional and stays optional.
Process
One tenant first. Then generalise.
01
Scoping against one real tenant
We pick one of your customers with real data volume and scope a single model against their schema. This is where we find out whether the feature is viable at all, before you have committed roadmap or told anyone it is coming. Two to four weeks.
02
Build and shadow deployment
Model, adapter, service, and monitoring built against that tenant, then run in shadow mode inside your product so you see live behaviour with nothing exposed to the customer. Four to eight weeks depending on schema complexity.
03
Generalise and hand over
Second and third tenant onboarded through the adapter layer to prove the mapping approach holds. Then documentation, runbook, and knowledge transfer to your engineers.
// FAQ
Questions vendors ask before signing an NDA.
Do you really work white-label?
Yes, and it is one of our most common setups. Under NDA, your customers never learn we exist, we do not list the work in our portfolio, and the deliverable carries your branding. We have shipped work this way for agencies and product companies both.
Who owns the code?
You do, in full, on final payment. All rights transfer. No licensing fees, no runtime dependency on our infrastructure, no clause that makes you come back to us to keep it running.
Do we have to move our data anywhere?
No. The standard deployment runs inside your infrastructure or your cloud account. For regulated verticals that is usually a requirement rather than a preference, and it is how we default to building.
Will you use an LLM for the scoring?
For tabular prediction, no - gradient boosting on your data, because the output has to be deterministic, auditable, cheap at volume, and defensible in a procurement review. We use LLMs where hallucination is recoverable: drafting text, summarising records, generating human-readable explanations of a score that was computed elsewhere.
What if the scoping phase says it will not work?
Then we tell you and we stop. Phase one is priced and delivered separately for exactly this reason. A conclusion of 'this tenant's data cannot support the model you want to sell' is a useful result and you pay only for phase one.
How do you price this?
Scoped per engagement after phase one, not before. Pricing depends on schema complexity, how many tenants need onboarding, and whether you want a monitoring retainer afterwards. We quote precisely once we have seen the data, and we do not quote before.
NDA first, then we look at one tenant.
Thirty minutes to work out whether the feature you want to sell is supportable by the data your customers actually have. If it is not, we will say so on that call.
Book a 30-min call