How much does it cost to build an AI app?

A production AI MVP typically ranges from $5,000 to $30,000 depending on scope. We scope your requirements first, then provide a fixed estimate. A document RAG chatbot sits at the lower end; a custom computer-vision pipeline or fine-tuned model sits higher.

How long does it take to build an AI MVP?

Most AI MVPs reach production in 6 to 10 weeks, across a six-phase pipeline: discovery and scoping, data preparation, prompt engineering and LLM integration, backend and API integration, testing and QA, and deployment.

Can you integrate AI into my existing app?

Yes. We wire LLMs, retrieval-augmented generation, or vision models into existing mobile and web products through your APIs, authentication, and database, with evaluation harnesses and production monitoring.

Which LLMs do you work with?

OpenAI, Anthropic (Claude), Mistral, and open-weight models. We choose per use case based on cost, latency, and accuracy rather than defaulting to one provider.

Do you build HIPAA-aware healthcare AI?

Yes. We have built HIPAA-aware healthcare products such as DentaSmart, with experience handling sensitive healthcare data and workflows under appropriate security and compliance considerations.

Do you work with US-based startups?

Yes. We have delivered products for US-based clients, and work with founders across the US, UK, UAE, and Singapore with working-hours overlap and async delivery.

Service spotlight · AI

Real-world AI systems,shipped to production.

AI app development that reaches production rather than stopping at demos. The work covers conversational agents and LLM integration, retrieval-augmented generation, vector search, computer vision, and custom model training and fine-tuning. Evaluation harnesses and monitoring are wired in from the first commit so the system holds at real traffic.

Conversational AI, LLM integration, vector search, and custom models. Built end-to-end and operated like the production systems they actually are, not the demos most agencies stop at.

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AI systems visualisation showing connected intelligence components

What we build

AI features that move past the demo.

Six AI capabilities we build into production systems: conversational AI and agents, LLM integration with evaluation harnesses, vector search and RAG, computer vision, custom model training and fine-tuning, and intelligent automation. Each capability is delivered end-to-end — architecture through deployment, monitoring, and iteration on real user data.

Six capabilities we ship into production systems, each picked up end-to-end from architecture through deployment and operation.

Conversational AI

Chatbots, agents, and copilots that actually understand context. Multi-turn dialog, tool use, memory, and the guardrails that keep responses on-brand.

Agents · RAG chat · Copilots

LLM integration

OpenAI, Anthropic, Mistral, or your own. Prompt engineering, evaluation harnesses, and the integration layer that connects models to real product flows.

OpenAI · Claude · Open weights

Vector search & RAG

Semantic search over your documents. Retrieval-augmented generation that answers from your real data, not the model's training set.

Embeddings · Pinecone · pgvector

Computer vision

Image analysis, object detection, classification, OCR. From medical imaging to retail product recognition, built on transfer-learned CNN architectures.

CNNs · VGG16 · ResNet · OCR

Custom models

Transfer learning, fine-tuning, and bespoke architectures when an off-the-shelf model doesn't fit. We train, evaluate, and deploy.

Fine-tuning · Transfer learning

Intelligent automation

AI-driven workflows that learn from feedback. Process automation that adapts to edge cases instead of breaking on them.

Workflows · Decisions · Feedback loops

How we ship AI

From concept to production in six phases.

A six-phase AI delivery pipeline: discovery and feature scoping, data preparation and knowledge base, prompt engineering and LLM integration, backend and API integration, testing and quality assurance, and deployment with post-launch optimisation. Each phase is scoped on actual constraints, and a production AI MVP typically reaches users in six to ten weeks.

The same pipeline we run on every serious AI build, refined over a decade of shipping production systems.

Discovery & feature scoping
We map the business problem to AI capabilities. What's actually achievable, what isn't, and what the smallest valuable shipping unit looks like.
Data preparation & knowledge base
Gathering, cleaning, and structuring the data that feeds the model. Quality here defines what's possible everywhere else.
Prompt engineering & LLM integration
Crafting prompts, designing system messages, choosing the right model, and wiring up evaluation harnesses for measurable iteration.
Backend & API integration
Wiring the AI into your existing systems: APIs, databases, auth, observability. The unglamorous work that makes it actually usable.
Testing & quality assurance
Evaluation runs, hallucination checks, edge-case handling, and adversarial probing. AI that's been stress-tested, not just demoed.
Deployment & post-launch optimisation
Production deployment, monitoring, cost optimisation, and iterating against real user feedback once the system is live.

Where we ship AI

AI products across the industries where it matters most.

AI products shipped across six industries: healthcare and life sciences, fintech and payments, retail and e-commerce, social platforms, telecommunications, and media and entertainment. Each build is adapted to its domain's compliance, data sensitivity, and latency constraints rather than fitted with a generic template, and integrates with existing APIs, auth, and databases.

Healthcare & life sciences

Diagnostic AI, clinician copilots, patient triage. Built for sensitive workflows with auditability baked in.

Fintech & payments

Fraud detection, KYC automation, conversational banking. Compliance-aware AI that knows when to escalate to humans.

Retail & e-commerce

Product search, recommendations, conversational shopping. AI that lifts conversion without spamming the catalogue.

Social platforms

Moderation, recommendation, content generation. AI tooling that scales with community size without breaking the vibe.

Telecommunications

Network anomaly detection, predictive maintenance, customer service automation. AI for systems that simply cannot go down.

Media & entertainment

Personalisation, content generation, semantic media search. Tools that make creators faster without replacing them.

FAQ

Frequently asked questions

How much does it cost to build an AI app?
A production AI MVP typically ranges from $5,000 to $30,000 depending on scope. We scope your requirements first, then provide a fixed estimate. A document RAG chatbot sits at the lower end; a custom computer-vision pipeline or fine-tuned model sits higher.
How long does it take to build an AI MVP?
Most AI MVPs reach production in 6 to 10 weeks, across a six-phase pipeline: discovery and scoping, data preparation, prompt engineering and LLM integration, backend and API integration, testing and QA, and deployment.
Can you integrate AI into my existing app?
Yes. We wire LLMs, retrieval-augmented generation, or vision models into existing mobile and web products through your APIs, authentication, and database, with evaluation harnesses and production monitoring.
Which LLMs do you work with?
OpenAI, Anthropic (Claude), Mistral, and open-weight models. We choose per use case based on cost, latency, and accuracy rather than defaulting to one provider.
Do you build HIPAA-aware healthcare AI?
Yes. We have built HIPAA-aware healthcare products such as DentaSmart, with experience handling sensitive healthcare data and workflows under appropriate security and compliance considerations.
Do you work with US-based startups?
Yes. We have delivered products for US-based clients, and work with founders across the US, UK, UAE, and Singapore with working-hours overlap and async delivery.

Got an AI feature on the roadmap?

Let's scope whatactually ships.

Free 30 minute call. We talk through the feature, the realistic constraints, and whether the build is best done with a frontier model, fine-tuning, or something simpler entirely. No pitch.