AI development services that survive production
An AI development company for products that already exist and for ones that do not yet: generative AI features, retrieval pipelines and agent systems, built to be measured rather than demoed. The multilingual enterprise platform we shipped cut manual processing by 87% at 99.2% extraction accuracy.
An LLM feature in your product: $15,000–$30,000, 2–4 weeks · a full AI product: $40,000–$120,000, 8–12 weeks · or hire AI engineers onto your team from $4,000 a month
Which AI development service fits what you have?
Six starting points, the build each one turns into, and what it costs. Every figure is a band we publish on the pricing page and quote against.
| What you have | What we build | Band | Typical time |
|---|---|---|---|
| A product, and a question it should answer from your own data | AI integration: an LLM or RAG feature inside it | $15,000–$30,000 | 2–4 weeks |
| A workflow where the model has to act on your systems, not only answer | An AI agent, one workflow at a time | $15,000–$30,000 per workflow; a multi-agent system at the top of $40,000–$120,000 | 2–4 weeks; 8–12 for a multi-agent system |
| Documents in formats or languages an off-the-shelf IDP tool cannot read | Custom AI document processing: extraction, validation, review and ERP sync | $40,000–$120,000 | 8–12 weeks |
| No product yet — the AI is the product | A full AI product or AI MVP: pipeline, frontend, backend and infrastructure | $40,000–$120,000 | 8–12 weeks |
| A product owner and a backlog; the gap is AI capacity | AI engineers on your team, in your repo and your standups | $4,000–$7,000 per engineer per month, three-month minimum | Start in 1–2 weeks |
| A sense that AI belongs in the product, and no answer yet to where | Discovery: a data audit, the architecture and a cost per answer | The first step of a build, not a separate engagement | Usually a week |
Between two rows? The AI development cost calculator asks the same questions and places the estimate inside the band.
Custom AI solutions we build
The capabilities behind those rows. Most of them start as generative AI — a model that answers or acts — and most of what decides whether they work is the plumbing around the model, not the model. Some problems still want a classifier rather than a chat window, and we build those too.
AI integration services
Generative AI features in a product you already run — chat, document Q&A, content generation, code assistants — on OpenAI, Anthropic, Google or open-weight models.
How an integration is built →RAG pipeline development
Retrieval-augmented generation that lets a model answer from your private data — documents, databases, knowledge bases — and point at the source it used.
AI agent development
Agents that call your tools and act on what the model says: tool use, multi-agent orchestration and human-in-the-loop checkpoints.
What a production agent needs →AI document processing
Ingestion, classification, multilingual extraction, validation rules and a review workspace, for the documents an IDP product gets wrong.
The system behind the 87% →AI MVP and AI SaaS development
Full-product development where AI is a first-class feature rather than a bolted-on afterthought. Multi-tenant and subscription-ready from the first schema.
How an AI MVP is built →ML models and MLOps
Custom models for classification, prediction, NLP and computer vision, and the infrastructure under them: model serving, vector databases, batch pipelines, cost control.
Enterprise AI solutions: what has to be true in production
Enterprise is not the size of the model. It is the list of things a pilot can skip and a system people rely on cannot. All six below come from one production system — the multilingual logistics platform, client anonymised — which is the only AI case on this site so far.
It writes into the system of record
Validated values go into the ERP the client already ran, with approval checkpoints kept for edge cases and exceptions — not into a dashboard beside it that somebody has to copy from.
Every value traces to a source
Analysts review by confidence, with the source passage highlighted and an audit trail behind each approval. Retrieval beat fine-tuning here because every answer had to point at a document someone could open.
Accuracy is measured, not estimated
A golden dataset across languages and document types from week one. The 99.2% extraction accuracy on production flows is that harness’s number; without the harness there would be no number to publish.
The running cost is known first
About $0.011 per answer across roughly 40,000 queries a month, at 1.9 seconds on average to the last token and 4.2 at p95. The cost per answer was worked out in the first week, and it is what made the business case.
Data stays where it has to
An NDA before any data is shared; isolation, access controls and encryption as standard; a fully self-hosted pipeline where data cannot leave your boundary. HIPAA-aligned architecture or SOC 2 evidence is real extra scope, quoted on top.
Five languages, one workflow
Fourteen sources in five operating languages run through one pipeline, with no separate workflow per region or business unit, over a corpus that is reindexed every day.
Do you need AI consulting before AI development?
You know AI should be in the product somewhere. You do not yet know where, or whether your data can carry it.
Usually a week, not a quarter. AI consulting here is the first step of a build rather than a separate engagement: a data audit on a genuine random sample of your documents — not the tidy examples — a definition of what a correct answer is, the architecture, and a cost per answer before anything is built. On the platform above that last figure came out at about $0.011, and knowing it in week one is what made the business case. If the audit shows the corpus cannot support the product, that is the most valuable thing the week produces, and we would rather say it then than in month three.
Use-case and data audit
Which question the model should answer, whether your data contains the answer, and what has to be cleaned, permissioned or reconciled first.
Architecture and cost per answer
Retrieval or fine-tuning, which model, self-hosted or API, and what one answer costs at your volume — written down before any code.
A scope you can take anywhere
A written scope inside 48 hours of the discovery call. Build it with us, with your own team, or with our AI engineers on your board.
AI tech stack we work with
Named by vendor rather than by model version: the models change every few months, and the code around them should not have to.
LLM Providers
Frameworks
Vector Databases
ML / Training
Backend
Infrastructure
How we deliver an AI project
The order the logistics platform was built in, which is the order we would build yours in.
Discovery and data audit
A random sample of your real documents, a definition of what a correct answer is, and a written scope inside 48 hours of the discovery call.
Golden dataset
An evaluation set across your languages and document types, built in week one, so accuracy is measured against real cases from the start rather than at the end.
Architecture and cost
Model selection, pipeline design and infrastructure planning, with the cost per answer written down before a single line of code.
Delivery in slices
The highest-volume flow first, in two-week sprints with live demos, instead of one big-bang rollout across every team at once.
Integration with checkpoints
Outputs wired into the systems you already run, with approval steps kept for the edge cases and exceptions a model should not decide alone.
Launch and monitoring
Accuracy dashboards, cost alerts and a feedback loop for prompt and retrieval changes after go-live, so a regression shows up on a chart before it shows up in a complaint.
How much does AI development cost?
The published answer, before you ask for one.
An LLM feature added to a product that already exists runs $15,000–$30,000 over 2–4 weeks. A full AI SaaS MVP — custom pipeline, frontend, backend and production infrastructure — runs $40,000–$120,000 over 8–12 weeks.
The spread inside that second band is almost never about which model you use. It is about how ready your data is and how many external systems the answer has to reach: clean, structured, permissioned data means a retrieval pipeline in weeks, while scanned documents in four languages with no metadata means the data work is the project. Managed delivery is quoted fixed-price after a discovery call, with a written scope inside 48 hours.
Running it is a separate and much smaller number: about $0.011 per answer on the platform above, passed through at cost — the AI token cost calculator works yours out from your model’s prices. And if what you need is capacity rather than a project, AI engineers on your team are $4,000–$7,000 per engineer per month.
AI development cost calculator · Every band we publish · where the money actually goes
Yarvixo delivered our AI platform in 10 weeks — on time, on spec, and with architectural decisions I could not have made better myself. Genuinely impressive team.
Common questions
What clients typically ask before starting an AI project.
Relevant case study
The production system every figure on this page comes from.
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Further reading
Longer answers on cost, architecture, and the decision teams get wrong first.