Custom LLM Development
Build and deploy large language models trained on your proprietary data for document processing, customer support, internal knowledge bases, and more.
We design, build, and deploy generative AI systems — from custom LLMs to RAG pipelines and AI agents — that solve real operational problems, not just demos.
50+
Clients
100+
Projects
5+
Years
98%
Satisfaction
What We Do
Generative AI is no longer a future technology — it is actively reshaping how businesses operate, communicate, and compete. At Foundrex, we go beyond wrapping a ChatGPT API. Our generative AI development service covers the full lifecycle: use case discovery, model selection, fine-tuning, retrieval-augmented generation, integration, and production deployment. Every system we build is grounded in your data, your workflows, and your measurable goals.
Services Included
Build and deploy large language models trained on your proprietary data for document processing, customer support, internal knowledge bases, and more.
Connect your existing data sources to a language model using retrieval-augmented generation so AI responses are always grounded in your latest information.
Embed generative AI capabilities into your existing software stack — CRMs, ERPs, web apps — without rebuilding from scratch.
Production-grade integration of OpenAI and Anthropic APIs with proper prompt engineering, rate limiting, cost controls, and fallback handling.
Adapt foundation models to your domain, tone, and tasks using supervised fine-tuning on your curated datasets.
Strategic advisory to identify the highest-ROI generative AI use cases in your business before you commit to development.
Why Foundrex
We build systems designed to run in production from day one. Every architecture decision accounts for scale, cost, and reliability.
We are not locked to one provider. We select GPT-4, Claude, Llama, Mistral, or custom models based on your requirements and budget.
We implement private deployments, VPC isolation, and data governance practices so your training data and queries never leave your control.
Every engagement starts with defining success metrics. We track token costs, response accuracy, user adoption, and business KPIs throughout.
How We Work
We map your business problem to an AI use case, assess your data readiness, and validate feasibility before writing a line of code.
We design the model stack, retrieval architecture, and integration layer, then review it with your team before building.
We build, fine-tune, and test the system iteratively with weekly demos so you see progress throughout.
We connect the AI system to your existing tools, set up monitoring, and document everything for your engineering team.
We deploy, monitor production performance, and run a 30-day optimisation sprint to improve accuracy and reduce cost.
Common Questions
Projects typically range from $15,000 for a focused integration to $150,000+ for a custom LLM system with fine-tuning and full deployment. We scope every project before quoting.
A ChatGPT integration takes 2–4 weeks. A full RAG pipeline with custom data connectors takes 6–10 weeks. A fine-tuned model with enterprise deployment takes 3–5 months.
Not always. Foundation models work without proprietary data for many use cases. But if you want AI that knows your products, policies, or domain deeply, your data improves accuracy significantly.
No. When using the OpenAI API, your data is not used for training. We also offer fully private deployments using open-source models where data never leaves your infrastructure.
Healthcare, fintech, e-commerce, SaaS, legal, logistics, and education. See our industry pages for specific use cases in each sector.
Book a free 30-minute consultation. We'll tell you honestly whether AI is the right solution for your problem.
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