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AI Engineering

Custom LLM Solutions

Build language models that understand your domain. We fine-tune and deploy AI assistants for document Q&A, code generation, and specialized business tasks that reduce manual work by 60% or more.

60%+
Manual Work Reduction
15+
LLM Projects Deployed
4-8
Weeks to Production
99.5%
Uptime SLA

Generic AI is not enough for serious business use

ChatGPT and Claude are impressive, but they do not know your products, policies, or processes. They hallucinate when asked about your specific domain. They cannot access your internal documentation or integrate with your existing systems.

The result? Teams spend hours fact-checking AI outputs, or worse, they give up on AI entirely and go back to manual processes. The productivity gains promised by AI remain unrealized.

Custom LLM solutions bridge this gap. We build AI that is grounded in your data, trained on your terminology, and integrated with your workflows. The difference is immediate: answers you can trust, in seconds rather than hours.

What We Build

Real applications deployed for real businesses. Each solution is tailored to the specific domain, data, and workflows of our clients.

Internal Knowledge Assistants

AI that answers employee questions using your company documentation, policies, and historical data. No more searching through SharePoint or asking colleagues for the same information repeatedly.

Example: A 200-person logistics company deployed an internal assistant that now handles 400+ employee queries daily about HR policies, operational procedures, and client-specific requirements.

Document Q&A Systems

Query thousands of documents in natural language. Extract insights from contracts, research papers, technical manuals, or regulatory filings without manual review.

Example: A pharmaceutical company uses our document Q&A system to query 10,000+ clinical trial documents, reducing research time from days to minutes.

Code Generation & Analysis

AI assistants trained on your codebase that understand your patterns, conventions, and architecture. Generate boilerplate, write tests, and explain legacy code.

Example: A fintech startup uses a custom code assistant that reduced their onboarding time for new developers from 3 weeks to 5 days by explaining their proprietary trading algorithms.

Customer-Facing AI

Support chatbots and sales assistants that genuinely understand your products, pricing, and policies. Not generic responses, but accurate, brand-consistent information.

Example: An e-commerce brand's AI assistant handles 70% of customer inquiries without human escalation, with a 4.6/5 satisfaction rating.

Our Approach

Building effective LLM solutions requires more than API calls. We focus on the full lifecycle from data preparation to production deployment.

1

Model Selection & Strategy

  • Evaluation of OpenAI, Anthropic, open-source models
  • Cost-performance analysis for your use case
  • Data privacy and compliance assessment
  • Build vs. buy recommendations
2

Fine-tuning & Customization

  • Domain-specific fine-tuning on your data
  • Prompt engineering and optimization
  • System prompt design for consistent behaviour
  • Output formatting and validation
3

Integration & Deployment

  • API development with authentication
  • Embedding in existing workflows
  • User interface development
  • Monitoring and analytics dashboard
4

Ongoing Support

  • Model performance monitoring
  • Feedback loop implementation
  • Iterative improvements based on usage
  • Documentation and team training

Why Custom Over Off-the-Shelf?

Pre-built AI tools are great for generic tasks. For competitive advantage, you need AI that is built specifically for your business.

Data Privacy

Your sensitive data stays within your infrastructure. No concerns about proprietary information being used to train third-party models.

Domain Accuracy

Models trained on your specific terminology, products, and processes. No hallucinations about your business. Accurate answers every time.

Full Control

Customize behaviour, integrate with any system, and evolve the solution as your needs change. You own the model and the roadmap.

Model Agnostic by Design

We build abstraction layers that let you switch between AI providers. No vendor lock-in. Maximum flexibility.

No Vendor Lock-in

Switch between OpenAI, Anthropic, or open-source models without rewriting code. Your architecture stays stable even as providers change.

Cost Optimization

Route queries to the most cost-effective model for each task. Use GPT-4o for complex reasoning, smaller models for simple classification.

Future-Proof

When GPT-5 or Claude 4 launches, integration is a configuration change, not a rewrite. Your investment is protected.

We Work With What Works

We are model-agnostic. We select the right foundation model based on your requirements: cost, latency, accuracy, and data privacy needs. Whether that is the latest from OpenAI, a fine-tuned open-source model, or a combination.

Our stack includes battle-tested frameworks for building production LLM applications: LangChain for orchestration, vector databases for retrieval, and robust APIs for integration.

Technologies We Use

OpenAI GPT-4o / o1
Anthropic Claude 3.5
LLaMA 3.1 / Mistral
LangChain / LangGraph
Hugging Face Transformers
OpenRouter
AWS Bedrock
Azure OpenAI Service

Common Questions

How much data do we need to train a custom model?

It depends on the use case. For RAG-based systems (document Q&A), you can start with existing documentation, even a few dozen documents. For fine-tuning, we typically recommend 500-5000 high-quality examples. We can help you generate training data if needed.

What about hallucinations?

Hallucinations are a real concern with LLMs. We mitigate this through retrieval-augmented generation (grounding responses in actual documents), confidence scoring, citation requirements, and human-in-the-loop validation for high-stakes outputs.

Can we use this with sensitive/regulated data?

Yes. We can deploy models within your own infrastructure (AWS, Azure, GCP) or use on-premises solutions. We work with clients in healthcare, finance, and legal where data privacy is paramount. We can also use Azure OpenAI Service or AWS Bedrock for enterprise compliance needs.

How long does a typical project take?

A proof-of-concept can be ready in 2-3 weeks. A production-ready system typically takes 6-10 weeks depending on complexity, integrations required, and the size of your data corpus.

Ready to build AI that understands your business?

Let's discuss your use case. We will help you understand what is possible and provide a clear roadmap to production.

Get in touch