Custom AI Development Services
Tailored AI solutions built around your business processes, products, and workflows.
Custom AI application development for businesses that need more than off-the-shelf tools — fine-tuned models, custom LLM applications, internal AI tools, and AI-powered features built into your existing product, scoped to your actual data and use case.
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CUSTOM AI DEVELOPMENT STACK
Technologies we build with
LLM application development
Custom applications built on top of Claude, GPT, and open-source models — internal tools, customer-facing features, and workflows powered by LLMs scoped to your specific problem.
Model fine-tuning
Fine-tuning models on your proprietary data when off-the-shelf performance isn't accurate or specific enough — for classification, generation, or domain-specific tasks.
RAG pipeline development
Retrieval-augmented generation systems that ground AI outputs in your actual documents, data, and knowledge base — reducing hallucination and improving trust in results.
AI feature integration
Embedding AI capabilities directly into your existing product — search, recommendations, summarization, or generation features shipped as part of your core app.
ML model development
Traditional machine learning models for prediction, classification, and forecasting when the problem calls for ML rather than a large language model.
AI infrastructure & MLOps
Model deployment, versioning, monitoring, and cost management infrastructure — so AI features run reliably in production, not just in a notebook.
Livrables
Nos outils de prédilection
CAPABILITIES
What we build
Internal AI tools
Custom tools built for your team's workflows.
AI-powered search
Semantic search over your data and documents.
Document processing & extraction
Automated data extraction from unstructured docs.
Recommendation systems
Personalized recommendations from your data.
Classification & prediction models
ML models for forecasting and categorization.
AI copilots & assistants
Embedded assistants inside your product.
Fine-tuned custom models
Models trained on your proprietary data.
RAG knowledge systems
AI grounded in your actual business data.
AI MVP development
Fast validation of AI-powered product ideas.
Ongoing AI development support
Retainer-based iteration and improvement.
Comment nous travaillons.
Un rythme mesuré — pas de surprises, des démos hebdomadaires, des décisions en quelques heures.
- 01
Discovery
Problem definition, data audit, and model evaluation.
- 02
Prototype
Proof of concept and model validation.
- 03
Build
Full development with iterative testing.
- 04
Deploy & Monitor
Production launch with monitoring and cost tracking.
Ce que vous obtenez réellement.
Équipe interne senior
Pas d'intermédiaires. Ceux qui définissent le projet sont ceux qui le livrent.
Démos hebdomadaires, décisions rapides
Sprints de livraison de deux semaines avec une démo publique chaque vendredi. Finies les devinettes.
Conçu pour durer
Stacks typées, tests, observabilité, design tokens — construit pour que la prochaine équipe vous remercie.
WHERE WE WORK
Serving clients worldwide
We partner with startups and businesses across the globe, with dedicated expertise in these markets:
Working across US, UK, European, Gulf, and Asia-Pacific time zones with weekly demos.
FAQ
Frequently asked questions
Cost & scope
A custom AI application build starts around $10k, including model evaluation, development, and production deployment. Complex fine-tuning or large-scale ML projects are quoted based on data and infrastructure requirements.
It depends on the use case. RAG systems and fine-tuning benefit from your proprietary data, but many applications work well with general-purpose models and prompt engineering alone.
A typical build takes 6-8 weeks from discovery through production deployment, starting with a prototype to validate the approach before full development.
Technical approach
We evaluate 3-5 candidate models or approaches against your actual data and requirements during discovery, rather than defaulting to whatever's trending — accuracy, cost, and latency all factor in.
RAG grounds a general-purpose model in your specific documents and data at query time. Fine-tuning actually retrains a model on your data. We recommend RAG first in most cases — it's faster, cheaper, and easier to update.
Yes — AI feature integration into an existing web or mobile application is one of our most common project types.
Reliability & support
Through RAG grounding, output validation, evaluation frameworks, and human review checkpoints scoped to how critical accuracy is for your specific use case.
30 days of post-launch optimization is included, with retainers available for ongoing model tuning, cost management, and feature expansion.