Custom AI development services
We build the AI feature and ship it to production
Our custom AI development services take a scoped feature or product and turn it into software running in your environment, with evals, guardrails and a cost ceiling. Working code every week, not demos.
Unlocking Tech is an AI engineering company based in Lisbon, Portugal, providing custom AI development services for SMEs and scale-ups — typically companies of 10 to 200 people. An engagement takes a scoped feature, product or agent and ships it as software running in your environment: senior engineers only, working code demoed every Friday, evals running in CI from day one, prompts in version control, and a cost ceiling enforced in code. Projects are fixed-price and scoped before any production code is written — most start between €15k and €50k and reach production in weeks, not quarters. You leave with code in your repo, a runbook and one agreed metric, and you own all of it.
Everything you need.Shipped to production.
No notebooks. No demos as deliverables. Just software running in your environment, with evals, guardrails and a cost ceiling you agreed up front.
A documented model choice
Frontier vs open-weight per task, with the cost, latency and quality trade-off written down against your data. Not a vendor default you can't argue with.
Evals running in CI from day one
A labelled test set scored on every change. A prompt or model swap that regresses quality fails the build, before it reaches a user.
Prompts and config in version control
Prompts live in the repo, are diffable and reviewed in PRs, with the model version pinned. No silent changes in a console nobody can trace.
A cost ceiling enforced in code
Per-request and per-tenant budgets, model-tier routing, caching and alerts before you hit the number. Agreed up front, not discovered on the invoice.
Guardrails on the user-facing path
Schema-constrained outputs, PII handling, injection and jailbreak mitigation, sane refusals on out-of-scope asks. It survives real users, not just the happy path.
Software you own, with the metric
Code in your repo, a runbook, and a dashboard tracking the one number we agreed to move. A clean handover your team can run without us.
Five steps.To production, fast.
From a thirty-minute call to a deployed feature with an owned metric, broken into milestones you sign off on.
Thirty minutes with an engineer on the call. You describe the feature, the data you have, latency tolerance and budget shape. We tell you honestly whether this is a build, an integration, or something you don't need us for.
Architecture, model choice with the trade-off written down, the eval set and target metric defined together with you, cost ceiling, milestones and price in writing. The metric is agreed here, not retrofitted at the end.
Senior engineers ship to a real environment on a weekly cadence, with a demo every Friday against the eval set. Prompts and config in version control from the first commit; evals running in CI.
Once the core path works: injection and abuse handling, cost-ceiling enforcement, observability, load behaviour and edge cases. The thing holds up under real traffic.
Production deploy, dashboard live, tuned until the agreed number holds. Then a clean handover with runbook and docs, or we stay on for Support & Scale if you want it.
We're not for everyone.We’re for the teams ready to ship it.
If any of these sound familiar, we should talk.
Founder with a stuck prototype
A GPT or Claude demo that won't survive production
- Hallucinations on real data
- Unpredictable inference costs
- No way to tell if a change helped or hurt
Outcome: A prototype that becomes software you can ship and trust.
Product or engineering lead
Adding an AI feature to a real codebase
- No in-house LLM, RAG or agent depth
- Real users and a real quality bar to meet
- Don't want to hire a permanent AI team for one initiative
Outcome: The feature shipped to your standard, then handed back clean.
CTO burned by an AI agency
Paid for a POC that was a notebook
- No tests, prompts edited live in a console
- Costs nobody could explain
- A team that says yes to everything
Outcome: Production software you own, and honest answers when AI is wrong.
Senior engineers. No handovers. No fluff.
AI solutions we build
Custom AI software development, for us, means five kinds of system, each judged on one metric agreed up front. LLM features and retrieval-augmented generation (RAG): assistants and search that answer from your own documents through a vector database, with the source shown. AI agents that act in your systems — read a file, score a lead, update the CRM. Computer vision: detection, inspection and OCR on the images you already collect. Natural language processing: classify, route and extract fields from email, contracts and invoices. Predictive analytics: forecasting, anomaly and fraud detection, and recommendations on your own data. Generative AI is one tool on that list, not the whole list.
Custom AI we have shipped
What custom AI engineering looks like in production, with the numbers from each case study. A precision-nutrition platform for neonatal care in the US that turns a proprietary clinical dataset into feeding recommendations at the bedside, integrated with Epic via HL7/FHIR: 94% protocol adherence after launch and feeding decisions 3× faster. A quality-management SaaS for medical-device makers whose regulatory assistant cites the clause each answer came from. An AI agent that qualifies B2B leads from 1,200 CSV files a month and pushes the hot ones into HubSpot — screening went from days to hours.
Fine-tuning, model training or a model API?
Most custom AI projects should start with a frontier model behind an API — OpenAI, Anthropic's Claude or Google's Gemini — plus retrieval over your data, because it is the fastest path to a measurable result. Fine-tuning earns its place when the eval set shows a gap that prompting and retrieval can't close, or when cost and latency at your volume call for a smaller model. Training your own model makes sense for narrow, high-volume tasks on proprietary data — tabular prediction, vision, anomaly detection — where you own labelled examples. We write the choice down with its cost, latency and quality trade-off before building.
From PoC to MVP to production
A proof of concept (PoC) answers one question: does this work on your data? We build it against an eval set of real cases, not a demo on hand-picked examples, and it ends in a written go or no-go. An MVP puts the feature in front of real users with guardrails, logging, human review where errors are costly and a cost ceiling in code. Production adds the rest: monitoring, drift and cost alerts, load behaviour, a runbook and the handover. Each step has an exit criterion tied to the agreed metric, so a failed PoC is a cheap answer rather than a sunk quarter — and a feature that passes reaches production in weeks, not quarters.
Our AI tech stack
The stack follows the problem and the systems you already run, but these are the tools we ship with. Models: OpenAI, Anthropic Claude and Google Gemini through their APIs, and open-weight models where data residency or cost calls for them. Machine learning: Python with PyTorch, TensorFlow, scikit-learn and XGBoost; OpenCV and YOLO for computer vision. Product: TypeScript, React and Node.js, or .NET, on PostgreSQL. Cloud: AWS, Azure or Google Cloud, usually the account you already have. n8n or Make when a workflow tool is enough. MLOps: evals in CI on GitHub Actions, versioned prompts and models, monitoring for drift and cost. Frameworks such as LangChain or LlamaIndex are a per-project choice, not a default.
How much does custom AI development cost?
Most of our custom AI development projects start between €15k and €50k, at a fixed price agreed before production code is written; tightly scoped builds come in below that and multi-feature products above €50k. Five things move the number: the state of your data (clean and reachable, or needing pipelines first); how many systems the AI has to read from and write to; the quality bar and how much human review it needs; the model path (an API with retrieval, fine-tuning, or training your own); and compliance work such as a DPIA or EU AI Act obligations. Inference and hosting are a separate running cost, capped in code.
Security, GDPR and the EU AI Act
Security reviews for enterprise AI ask three questions — where the data goes, who can see it, and what the model is allowed to do — and we answer them in the design, not after launch. The system runs in your environment; personal data is checked against the GDPR before it reaches a model provider (lawful basis, data-processing agreement, EU region where residency matters), with PII handling and an audit log of what the system did. Under the EU AI Act, each use case gets a risk classification at scoping: most business AI — document extraction, internal assistants, B2B lead scoring — is minimal or limited risk, where the duties are transparency and staff AI literacy; hiring, credit scoring of individuals or AI in medical devices is high-risk and brings conformity obligations. We flag what needs your lawyer.
What does an AI software development company do?
An AI software development company builds custom software with AI inside it — features and products designed for one company's use case and shipped to production, not sold off the shelf. Unlocking Tech is an AI software development company that builds those AI features and products for your specific problem, with evals, guardrails, and a cost ceiling enforced in code. Unlike most artificial intelligence software development companies, we keep prompts and config in version control and hand you the code and one agreed metric — you own both.
When the AI sits inside a bigger product
Most AI features live inside software that also needs accounts, permissions, billing, dashboards and integrations. When the project needs it, we build that around the AI — the hospital-management and agritech platforms in our case studies were full product builds — and hold it to the same standards: version control, tests in CI, code you own. If your project is mostly product software with little or no AI in it, start with our custom software development team instead.
Our AI software development service
Our AI software development service is a scoped build run by senior engineers: we agree the problem and the metric, ship working code weekly, and hold it to production standards with evals, guardrails, and version-controlled config. Where most generative AI development companies stop at a demo, we ship customized AI solutions you can put real load on — and you own the code. Engagements are project-based, typically €15k–€50k and up.
A machine learning development company that ships models to production
As a machine learning development company, we build and train models that make predictions or decisions on your data, then run them as a live part of your product. Most machine learning companies stop at a notebook. We build the model, the evals that prove it works, and the monitoring that catches drift after launch. You get production code you own, not a demo that breaks the first time real data hits it. Among machine learning development companies, that is the line we hold: versioned data, reproducible training, and a clear path from prototype to the thing your users actually touch. That is how a serious machine learning business has to run.
Data engineering: the pipelines your models run on
As a data engineering company, we build the plumbing that gets data from where it lives to where your models and analytics can use it: ingestion pipelines, warehouses, and feature stores. A model is only as good as the data feeding it. So before we train anything, we build the pipelines, schemas, and feature stores that keep that data clean, fresh, and queryable. Unlike data engineering companies that hand you a diagram and leave, we ship the running pipelines and the code behind them, instrumented so you can see the moment something breaks.
How do you choose a custom AI development company?
Choose a custom AI development company — or any AI development partner — on evidence of production discipline, not portfolio screenshots. Ask three things: how quality is measured before launch — the answer should be an eval set built on your data; how changes are controlled — prompts and config in version control, reviewed like code; and what happens when the model is wrong — escalation to a person, not silence. A vendor who answers those in writing is selling engineering. One who answers with a demo is selling the demo.
What should artificial intelligence development services include?
Artificial intelligence development services should cover the full path to production: scoping the problem to one metric, choosing and justifying the model, building the feature with evals running in CI, hardening it with guardrails and a cost ceiling, and handing over code you own with a runbook. Many offerings stop at the prototype and leave the reliability work — the part that decides whether the system survives real use — to you. Price the whole path, not the demo.
Red flags when buying AI development services
The red flags repeat: a proposal without a metric or an eval plan; pricing that rewards time spent instead of a result; prompts edited live in a console nobody can audit; ownership of code and models left vague in the contract; and a yes to every idea. Buyers often come to us after an AI tool nobody trusted enough to keep using — usually because nobody defined what working meant before the build started. Define it first. It is the cheapest part of the project.
Custom AI development vs the alternatives
Where custom AI development services fit against buying an off-the-shelf tool or hiring in-house — and where they don't.
Custom AI development What we build | Off-the-shelf AI tool | In-house AI hire | |
|---|---|---|---|
| Fits your workflow and data | ✓ | If your process matches the tool's | Yes, over time |
| Time to production | Weeks, in milestones | Days to configure | Months — hiring comes first |
| Quality measured on your data (evals) | ✓ | — | If you build the discipline |
| Running cost | Ceiling enforced in code | Per-seat licence that grows with the team | Salaries, fixed |
| Who owns the code and the models | You | The vendor | You |
| When the vendor changes course | Code, runbook and docs are yours | You migrate | Not applicable |
| Best for | A feature or product with a metric to move | A generic job the tool already does well | AI as your core product, long term |
Selected work
Frequently asked questions
A custom AI development company builds AI features and products for your specific use case, rather than selling you a generic tool. Our custom AI development services take a scoped problem — a feature, an internal product, an AI agent — and ship it as software running in your environment, with evals, guardrails and a cost ceiling. The difference from generic 'AI development services' is that everything is built against your data, your systems and one metric you agreed up front.
Project-based, scoped and fixed before we write production code, not hourly staff augmentation. As a shape, most engagements start in the €15k–€50k range, smaller scoped builds below that, larger multi-feature products above €50k. Five things move the number: the state of your data, the number of systems to integrate, the quality bar and human review, the model path, and compliance work. The running cost ceiling for inference is a separate number we design to and enforce in code.
Yes, when the open question is whether a model can do the job on your data at all. A PoC with us is built against an eval set of real cases and ends in a written go or no-go against the metric we agreed — not a demo. If it passes, it becomes the first milestone of the MVP instead of throwaway code. If the answer is already obvious, we skip it and scope the MVP directly.
Hire a company when you need one AI feature or product shipped and measured, and don't yet know whether AI will be core to the business — it is faster than recruiting and you keep the code. Build an in-house AI team when AI is the product itself and the work is permanent; hiring comes first and takes months. A common middle path: an external team ships the first system, hands it over with a runbook, and you hire once the volume justifies it.
You see working software in your environment within the first weeks, with a demo every Friday, not a big-bang reveal at the end. A focused single feature is typically a few weeks to production; a multi-surface product is longer and broken into milestones you sign off on. We don't show notebooks and call it progress.
We don't do staff augmentation, ship demos as deliverables, edit prompts in a console with no version control, or build without evals and a cost ceiling. We don't put juniors on your project. And if a deterministic rule or a plain integration solves the problem cheaper than AI, we tell you and point you there.
AI Strategy decides what to build and the roadmap, no code. Integration wires AI into systems and data you already run. Automation strings workflows and agents together to remove manual steps. AI Development is where the production software gets written: the custom feature or product, with evals, guardrails, cost ceilings and an owned metric.
Ask to see the engineering, not the deck. Five things separate a serious vendor: a written eval plan — how quality will be measured on your data before launch; prompts and config in version control, not edited live in a console; a cost ceiling designed into the build rather than discovered on the invoice; a contract that says you own the code and the trained models; and at least one story of telling a client not to build something. Many buyers reach us after paying for AI that did not survive real work — the fastest filter is asking a vendor how they know when their own system is wrong.
Six things in writing: the metric the build will be judged on; the eval set and how it is scored; the model choice with the cost, latency and quality trade-off stated; the running cost ceiling at your real volume; milestones with working software at each one; and code ownership with handover terms. A proposal that only lists features and a day rate is selling effort, not an outcome — and effort is what stalls after the demo.
Fixed price for scoped builds. It forces the scoping work to happen up front and puts the risk of vague requirements on the vendor, who is best placed to remove it. Time and materials makes sense when the scope genuinely cannot be known yet — early research or a discovery phase — but used for delivery it rewards slow work. We price per project, fixed before production code is written, with the inference cost ceiling as a separate number we design to. A vendor who cannot fix a price usually has not scoped the problem.
A pattern we hear again and again from buyers on their second attempt: the tool worked in the demo and failed in the workflow. A document-processing system that misread too many real documents to be trusted. An estimation tool abandoned because it needed a dedicated person full-time and created more work than the manual method it replaced. The common cause is a build judged on a demo instead of a metric — no eval set on real data, no error-rate target, no design for the cases the model gets wrong. It is why we agree the metric and the eval set before writing production code.
Yes — and for some processes you should insist on it. Where an error has fiscal or legal consequences, human review of low-confidence cases is a design requirement, not an admission of failure. We build it in deliberately: confidence thresholds, an approval queue, and an audit log of what the system did and what a person corrected. Those corrections feed the eval set, so quality is measured against real cases rather than a demo.
Both, when the project needs it. Most of our work is custom AI development — LLM features, RAG, agents, computer vision and predictive models — but an AI feature usually needs accounts, permissions, dashboards and integrations around it, and we build those too, to the same standard: version control, evals and tests in CI, code you own. For projects that are mostly product software with little AI in them, the product-engineering side of our services is the better starting point.
Concrete things: custom AI features inside your product, internal tools, full SaaS products, and agents that do real work. Everything ships with evals, guardrails, and a cost ceiling enforced in code, not in a slide. You own the code and one agreed metric we move.
It's a scoped, project-based custom AI build: we agree one metric to move, then ship working code every week against it. Most engagements run €15k–€50k+ depending on scope, and you own the code at the end. No retainers for seats, no per-head billing.
Yes. We build and train machine learning models and ship them into production software, with evals to measure quality and monitoring to catch drift once they're live. You own the code and the trained models. We don't hand over a notebook and call it done.
Yes. We build the ingestion pipelines, warehouses, and feature stores that your models and reporting depend on. If your data is scattered or unreliable, we build the pipelines that make it usable before any model goes near it.
Data engineering builds and maintains the pipelines, warehouses, and feature stores that move and store data reliably. Data science uses that data to build models that predict or classify. Data analytics interprets the data to answer business questions and report on what happened. Engineering makes the data usable, science builds models from it, analytics draws conclusions from it. We do the engineering and the model-building; we leave standalone BI reporting to analytics specialists.
Start your deployment.
Talk directly to a principal engineer.
No sales team.
No discovery workshops.
No procurement circus.
We scope, build and ship.
- Reply within 24h
- Engineer-led assessment
- Written proposal
- Lisbon · UK time zone
No commitment. Just an engineer.