Custom AI development
A custom AI development company you can hold to a written standard
Hiring a custom AI development company means the system is shaped around your workflow and data rather than another vendor's product roadmap, and that the result belongs to you. Because the system does not exist yet, we write down the acceptance conditions before anything is built.
- trigger: supplier renewal uploadedmatched to the vendor recordrule
- model: extract_clauses(agreement)parties, renewal, liability, datamodel
- reason: compare the terms with your policyliability cap below policymodel
- check: evaluation gate on the draftpassed, clauses 4.2 and 9.1 citedrule
- gate: legal signs off before signaturesent to legal with the draftperson
- trigger: claim filed with photosfirst notice of lossrule
- model: read_policy(claim)cover and limits readmodel
- reason: grade the damage from the photosseverity written to the claimmodel
- gate: the adjuster decides the payoutrouted with the photos and the gradeperson
- done: claim routed and loggedthe decision stays the adjuster'srule
- trigger: weekly sales data landswarehouse table refreshedrule
- model: forecast(store, item)next six weeks per itemmodel
- check: error against last month's actualsinside the agreed barrule
- gate: a planner signs off unusual ordersheld until approvedperson
- done: orders sent to the ERPeach change on recordrule
- Years building AI systems
- 6
- AI engineers
- 30+
- Ownership model
- Your team
- Acceptance conditions
- 8
Built from parts you own
The models, runtimes and stores a custom build is assembled from
Model API
OpenAIModel API
Mistral AIOpen weights
Hugging FaceFine-tuning
PyTorchTraining
Serving- Model API
- OpenAIModel API
- Mistral AIOpen weights
- Hugging FaceFine-tuning
- PyTorchTraining
- Serving
OllamaOn-premises
ONNX RuntimeOn-device
PostgreSQLApp data
MongoDBDocuments
ElasticsearchSearch
GitHubYour repository- OllamaOn-premises
- ONNX RuntimeOn-device
- PostgreSQLApp data
- MongoDBDocuments
- ElasticsearchSearch
- GitHubYour repository
The decision
When hiring a custom AI development company is the right call
Hiring a custom AI development company can provide fit, because the system follows the way your business works, and ownership, because the model configuration, code and data assets remain under your control. Those choices shape long-term cost and how easily your team can change direction.
Custom development is not always the most proportionate choice. If your process is common, an established product may meet the need sooner. If the available data is limited, a data-readiness project may create more value than an immediate model build. And if your team does not yet have a long-term owner for the system, we recommend agreeing that responsibility before development begins.
For many teams, the best answer combines products and custom work: buy the common capability, build only the part that is genuinely specific, and connect them carefully. The five engagement shapes below follow that principle.
- What we build
- The part that is yours
- Systems shaped by a workflow, a dataset or a constraint that no product covers, connected to the products that cover the rest.
- What we do not
- Rebuilding a product that already fits
- If a product already does it, we will name the product. We would rather build the integration than rebuild the thing.
- What you own
- All of it, from day one
- Code, models, fine-tuning data, prompts and infrastructure definitions, in your repository and your cloud account from the first week, not at the end.
- What the first month produces
- Something running
- A narrow version against real data, plus the acceptance conditions signed, so the thing being judged and the standard judging it both exist early.
01 · Engagements
Five ways a custom AI development company gets hired
Every engagement shape our custom AI development company offers carries a starting price and the first thing that lands on your desk. The second column matters more than the first: it is what you hold if the engagement stops after a month.
Custom AI software development
A system built into your existing product or platform, where the AI part is one component of something larger that already has users.
AI app development company
A standalone application, web or mobile, where the AI is the reason the application exists rather than a feature inside it.
AI MVP development
The smallest version that settles the question you are actually asking, built to be thrown away if the answer is no.
Enterprise AI development
A system that has to pass a security review, fit an existing control framework and survive a procurement process.
AI product development
A product you intend to sell, where the cost per user and the margin at scale are engineering constraints from the first week.
02 · Definition of done
What done means on a custom AI development build
On a custom build, you are commissioning something that does not exist yet. A demo shows that one path can work; written acceptance conditions explain what the finished system must do across representative cases. Agreeing those conditions in advance gives your team a fair, shared basis for review.
These are the conditions our custom AI development company signs up to. They go into the statement of work before the first commit, with the specific numbers filled in for your build, and we are held to all eight. If we ever want one of them relaxed, that is a conversation before the build rather than a discovery at the end of it.
- 01
A quality bar, in a number
The accuracy, precision or pass rate the system has to reach on a held-out set drawn from your data, agreed before we build and measured on data we have never seen. Not a benchmark score, and not a demo.
- 02
The test set is yours
You hold the evaluation set and can rerun it. A quality bar we grade ourselves against data we chose is not a condition, it is a claim.
- 03
Tests that cover the failure paths
Not a coverage percentage. Named tests for the cases that break this kind of system: malformed input, a model that times out, an empty result, a rate limit, and every case where the system is supposed to refuse.
- 04
It builds from a clean clone
An engineer who has not seen the project clones the repository, follows the readme and gets it running locally. If that does not work, the build is not complete, regardless of what runs on our machines.
- 05
Infrastructure as code, in your account
Everything it runs on is defined in files in your repository and deployed into your cloud account. Important configuration is documented in code rather than existing only as an undocumented console setting.
- 06
A runbook for the things that go wrong
What to do when the model provider is down, when quality drops, when costs spike and when a customer asks why the system did what it did. Written for your on-call engineer, not for us.
- 07
Monitoring that alerts a person
Quality, latency, error rate and spend, on a dashboard you own, with thresholds routed to a named person or team. Clear ownership helps changes get noticed and addressed early.
- 08
Handover, then a quiet period
Your engineers run it while we are still here and still paid, for a period agreed at the start. Handover is a thing that happens over weeks, not a document delivered on the last day.
03 · Stack
The stack our custom AI development company builds on
Models
- OpenAI
- Anthropic
- Meta Llama
- Mistral
- Your own fine-tune
Chosen per task against your data, not once for the project. The system is written so the model behind a step can be swapped without rewriting the step.
Application
- TypeScript
- Python
- Next.js
- FastAPI
- React Native
- Postgres
Boring on purpose. The interesting part of a custom AI build should be the model behaviour, never the framework.
Data and retrieval
- Postgres and pgvector
- Pinecone
- Qdrant
- Elasticsearch
- dbt
- Airflow
Whatever your data already lives in comes first. Adding a second database to a company that has one is a cost most builds do not need.
Serving and scale
- AWS
- Azure
- Google Cloud
- Kubernetes
- Serverless functions
- Modal
Your account, your region. Where a model has to stay inside your network we say which options that removes before you choose it.
Evaluation
- Held-out sets
- Regression suites
- LLM-as-judge with human sampling
- Tracing
Set up in week one, because the acceptance conditions above are meaningless without something that measures them continuously.
Operations
- Terraform
- GitHub Actions
- OpenTelemetry
- Grafana
- Sentry
- Cost dashboards
Spend is monitored beside latency and errors. On an AI system the bill is an operational signal, not a finance problem.
Delivery
How a custom build runs, week by week
Five steps in how our custom AI development company runs a build. Each one ends with something you hold, so an engagement that stops early still leaves you with more than an invoice.
- 011 week
Scope and acceptance
We work out what is genuinely custom and what you should buy instead, then write the eight conditions above with your numbers in them.
A fixed quote and a signed definition of done.
- 021–2 weeks
Data and feasibility
We look at the data you actually have rather than the data the project assumes, and test the riskiest assumption first.
A held-out evaluation set and a written answer on whether the quality bar is reachable.
- 033–6 weeks
Narrow build
One path end to end against real data, including what happens when it fails, in your cloud account from the first deploy.
Something running, plus the first measured score against the bar.
- 041–2 weeks
Widen and harden
The rest of the cases, the security review, the monitoring, the runbook and the cost work.
The system against all eight acceptance conditions, with the evidence for each.
- 05Agreed in scope
Handover
Your engineers run it while we are still on the engagement, and we fix what that surfaces.
Your team operating it without us, and the repository they can keep building on.
05 · Industries
Custom AI development by sector
- InsurancePricing and claims decisions that have to be explainable to a regulator years later, which rules out most of what gets demonstrated.
- BankingModel risk governance already exists here, so a custom build has to arrive with its documentation rather than acquire it later.
- HealthcarePatient data that cannot leave the network, which decides the model choice before anything else is decided.
- ManufacturingPlant systems with no API and a shop floor where an unreliable system is switched off within a week of going live.
- Supply chainThe useful data belongs to counterparties, so the first constraint on a custom build is contractual rather than technical.
- LogisticsThin margins and high volumes, where cost per transaction is an architecture decision rather than a finance one.
- RetailSeasonal peaks that size the system, and customer-facing experiences where answers need careful review and a clear route to support.
- ConstructionProject-based work with no shared system of record, so the honest first build is usually the one that creates one.
06 · Work
What our custom AI development company has built
- Wildlife monitoring · Listening stations and their appAverage station set-up: from nearly 25 to 30 minutes to under 10BirdNET models adapted to name birds in live, noisy field audio, on stations the client's own customers set up from a mobile app, without a technical installation.BirdNETReact NativeFastAPIRead the case study
- Optician training · AI patient for role-playAverage role-play session: about 25 minutes down to under 15An AI patient that plays a consultation inside a written scenario, with a stage classifier and feedback scored against the training guide, split across two models to keep the running cost down.OpenAIElevenLabsNestJSRead the case study
07 · The choice
Custom AI development company, dev shop or your own hires
| Meronym | A general dev shop | Hiring in-house | |
|---|---|---|---|
| Time to something running | Weeks. Step three is a working narrow build. | Depends on the team's AI experience, the scope and access to your systems. | Months before the first hire starts, longer before a team forms. |
| Who owns the model and the code | You, from week one, in your repository and your cloud account. | Usually you; confirm repository, cloud and deployment access in the contract. | You, entirely. |
| Who is accountable for accuracy | A named engineer, against the eight conditions above. | Confirm who owns evaluation, the quality threshold and ongoing monitoring. | Your team, once it exists and has learned what to measure. |
| What happens when it is built | Handover plus a paid quiet period, then your team runs it. | Often a support retainer or a planned handover, depending on the agreement. | They keep running it, which is the strongest argument for this column. |
| Cost shape | A fixed build against a written scope, then a run cost you can forecast. | Often time and materials; the total moves with scope and technical uncertainty. | Salary, which is the highest cost in year one and the lowest by year three. |
| When a team member leaves | The runbook and the clean-clone condition are why this is survivable. | Continuity depends on documentation, shared access and the handover arrangement. | The known risk of this column, and the reason people hire agencies. |
| Changing direction mid-build | A new scope and a new fixed quote. Honest, and slower than a conversation. | Often flexible under time and materials, with cost reviewed as the scope changes. | Often quicker to discuss internally, subject to the team's other commitments. |
The last column wins more often than a page like this usually says. If you intend to build several AI systems over the next few years, hiring is the right answer and the best use of a custom AI development company is the first build, with two of your engineers in the room, so your team starts from a working system instead of a blank repository.
The pod
Who works on a custom AI development project
Five roles on a typical build by our custom AI development company. The people who scope it are the people who write it, and you have their names before a contract exists.
| Role | Owns | Allocation |
|---|---|---|
| Tech lead | The architecture, the acceptance conditions and every decision that is expensive to reverse | Set in scope |
| AI engineer | Model choice, prompts or fine-tuning, the evaluation set and the cost per call | Set in scope |
| Product engineer | The application around the model, and everything the user actually touches | Set in scope |
| Platform engineer | Your cloud account, the deployment path, monitoring and the runbook | Set in scope |
| Delivery lead | Scope, the fixed quote, your people's time and what stays out of scope | Set in scope |
Read before you hire a custom AI development company
All guides- GuideAI agent costWhat a custom build costs to make and to run, by tier, and the factors that move the number more than anything else.
- GuideAgentic AI companiesHow providers compare on published client evidence, human review and the questions worth asking before a shortlist becomes a contract.
- GuideSelf-hosted AI agentsAgent tools you can run on your own servers: where each model runs, what each sends home, and what a private model costs to host.
FAQs
Questions buyers ask a custom AI development company
Straight answers on what a custom AI development company costs, who owns the result, what you should buy instead, and what happens when the build is finished.
How much does a custom AI development company charge?
An AI MVP typically starts around $8k, a production custom application around $12k, and an enterprise platform around $26k. Those are build starting points against a written scope. Running costs are separate and depend on usage, hosting and support; we model them before you commit.
Why choose custom AI development services instead of buying an off-the-shelf tool?
Custom work is most useful for fit and ownership: your workflow is genuinely specific, or control of the code, data and long-term cost matters. If an established product already meets the need, we will recommend considering it and can help with the integration rather than rebuilding the same capability.
What should I look for in a custom AI development company?
Look for four things early: indicative pricing, written acceptance conditions, clarity about who will build the system, and a willingness to recommend a product or data-readiness step when custom development is not yet justified. Specific answers make technical and procurement review much easier.
Who owns the model, the code and the training data afterwards?
You own all three, and it is in the contract rather than in a sentence on a website. The repository is yours from the first week, the infrastructure is defined in code in your own cloud account, and any data used for fine-tuning stays yours and is not reused for another client. Where a build sits on a third-party model, you own everything around it and we name what that dependency actually is, including what it would take to move.
Do you build on foundation models or train from scratch?
Almost always on foundation models, because for most problems training from scratch spends a year and a lot of money to arrive somewhere behind where you could have started. Fine-tuning is worth it when you need a consistent format, a specific tone or a narrow classification task with plenty of labelled examples. Training a model from nothing is worth it in a small number of cases, usually when the data is genuinely unlike anything public. We will say which of the three your case is, and the second answer is the most common.
How long does a custom AI development company take to build a system?
Scope and acceptance usually take one week, data and feasibility one to two weeks, and a narrow working version three to six weeks. Widening, hardening and the security review commonly add one to two weeks. Handover length is agreed during scope so your engineers have time to operate the system while we remain available.
What data do we need before we can start?
Less than most people assume for a system built on a foundation model, and more than most people assume for anything fine-tuned. The feasibility step exists to answer this specifically: we look at what you actually have, build a held-out evaluation set from it, and tell you in writing whether the quality bar is reachable. If it is not, that is the end of the engagement and you keep the evaluation set and the answer.
Will it work with the systems we already run?
That is usually most of the work. A custom AI system that cannot read from your warehouse, write to your system of record and authenticate the way your company authenticates is a demo. Integration is designed in the first step rather than discovered in the last, and where a system has no API we say so while the scope is still being written, because it changes the number.
What happens after the build is finished?
Your team runs it, and the definition of done exists so that this is possible. The clean-clone condition, the runbook, the monitoring and the paid quiet period are all there to make handover real rather than ceremonial. If you would rather we kept operating it we will, priced separately, but that should be a choice you make and not a dependency you discover.
How is this different from hiring an AI development company for a fixed project?
Mostly in what is fixed. Plenty of firms fix the price and leave the outcome vague, which moves all the risk of a vague scope onto you. We fix the price and the acceptance conditions together, so the scope has to be specific enough to be testable before anybody signs. It makes the scoping step slower and it is the reason the number holds.
Tell us what you are building, and what you have already tried.
One paragraph is enough. An engineer replies within two business days with whether this should be custom at all, which engagement shape fits, a starting figure, and the acceptance conditions we would put against it.
Reply within two business days · We will name the product if one already does it · You own the code from week one