AI engineering
The demo already works. The question is what happens next
Our engineers have taken AI past the pilot into products people rely on: insurance audits, oncology trial matching and a phone line that books repairs. You meet each engineer before they join.
What you are getting
4 things that decide this
- 01Senior engineers whose AI work is running in named products on this site, not held in a notebook waiting for a decision.
- 02They build the boring half: evaluation, fallbacks, cost control and the audit trail. That half is what decides whether a pilot survives contact with users.
- 03You interview every engineer, and turning one down costs you nothing.
- 04Prompts, pipelines, evaluation sets and infrastructure are yours from the first commit.
Why AI pilots stall, and what an engineer changes
A demo has to work once, in front of a friendly audience. A product has to work on the worst input a stranger sends on a bad day. Almost all the engineering sits in that gap.
PremiumAudit runs Claude against insurance audit documents. TrialTriage matches oncology patients to trials with a nurse approving every result and an audit trail across 23 tracked action types. Go4Gr8 carries live coaching sessions where an assistant tracks the commitments a leader makes.
None of those could ship as a clever prompt. Each needed a way to tell whether the answer was good, a plan for when it was not, and a human placed where the stakes required one.
What separates a pilot from a product
A way to measure quality
A scored set of real cases, run before every release. Without one, quality is whatever the last person to look at it felt.
Behaviour when the model is unsure
A refusal, a fallback or a handoff to a person. A system with no way to say it does not know will invent something instead.
A human where the stakes need one
Approval steps placed by consequence, as with the nurse review on TrialTriage. Not everywhere, which nobody would use, and not nowhere.
Cost and latency held down
Caching, smaller models for easy work, and knowing which calls justify the expensive one. Costs that scale with usage surprise people in month three.
An answer for the model being retired
Model versions get deprecated on a published schedule. The eval set is what turns that from an incident into a scheduled swap.
- InputCleaned and checked first
- RetrievalThe facts it may use
- ModelThe part everyone talks about
- CheckIs this answer usable
- HumanWhere the stakes require it
- RecordWhat it did, and why
The model is one box. Teams that budget for that box alone are the ones still stuck in pilot a year later.
AI systems carrying real consequences
“I am extremely happy with the results and would highly recommend Hashlogics to anyone.”
Daniel Khin · CEO, PremiumAudit.io
How hiring works
- 01
Tell us what the pilot proved
A free call about what works, what it gets wrong and who is waiting on it. If your problem does not need a model, you will hear that on the call.
- 02
Meet the engineers
We shortlist people who have carried AI into production, and you interview them against your own bar.
- 03
They embed
Your repository, your standups, your environments. One of our engineers owns the evaluation set and reports what it shows.
- 04
They hand over
The evaluation set, the pipeline and a runbook for the day a model version is retired, with someone on your team trained to use them. Where a client prefers we keep watching quality, we stay on under a service level we agree.
Stack
Models
Around them
Practices
Bring us the pilot that has stopped moving
Show us what it does well and where it embarrasses you. The scoping call is free, and you will leave with a straight read on what stands between it and real users.
01What is the difference between an AI engineer and a data scientist?
An AI engineer builds the system that runs in front of customers; a data scientist mostly answers questions with data. The engineer's work is retrieval, evaluation, fallbacks, cost and the plumbing that keeps a model useful under real traffic. If you need a model trained from scratch, that is a different hire and we will say so.
02Do you train your own models?
Rarely, because most business problems are solved better by retrieval and good engineering around a strong general model. Training earns its cost when you have a large, clean, proprietary dataset and a task no general model handles. We tell you which situation you are in before anyone spends money.
03How do you stop the system inventing answers?
Ground it in your own data, then give it a way to say it does not know. Retrieval supplies the facts, the prompt and checks constrain the shape, and a low-confidence path routes to a person instead of guessing. TrialTriage puts a nurse in front of every result for exactly this reason.
04What happens when the model version we built on is retired?
You run the evaluation set against the replacement and read the differences before switching. Deprecation is announced in advance, so this is a scheduled task rather than an emergency, provided somebody built the set. That artefact stays in your repository.
05Can they work alongside our own data team?
Yes, and that is the common arrangement. Your team usually knows the data and the business rules; ours brings production patterns for models, evaluation and failure handling. They join your standups rather than running a separate track.
06What actually drives the cost of an AI build?
The state of your data and how much a wrong answer costs. Clean sources and low stakes move fast. Messy documents plus a regulated decision means more review, more evaluation and more design. Scoping calls are free. Where we must go into an existing codebase to answer honestly, a paid two-week diagnostic produces a fixed price.
