Which model, which provider?
The model landscape changes every month, and the wrong choice locks in cost or quality problems.
LLM Integration & Optimization
LLM integration, fine-tuning and cost optimization
We integrate large language models into your products and internal systems through direct APIs, Amazon Bedrock, Azure OpenAI or self-hosted open-weight models. Then we make them reliable and affordable with prompt engineering, structured outputs, caching, routing and, where it pays off, fine-tuning.
What you can expect
- Model choices backed by evaluation, not hype
- Lower latency and token spend
- Enterprise-grade access, logging and data residency
(01) The problem
Why teams come to us for LLM Integration & Optimization.
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Unreliable outputs
Free-text responses break downstream systems that need structured, validated data.
Runaway costs
Long prompts resent on every call quietly multiply your bill.
Security and residency concerns
Legal and IT need control over where data goes and who can use which model.
(02) Use cases
What we build with LLM Integration & Optimization.
- 01
Amazon Bedrock integration
Production Bedrock integrations with session management, delivered over a 15-month engagement for a cloud-automation company.
- 02
Structured extraction APIs
LLM endpoints that return validated JSON your systems can trust.
- 03
Prompt engineering and optimization
Systematic prompt design, testing and versioning for quality and cost.
- 04
Model migration and routing
Move between providers, or route requests by complexity to cut cost without losing quality.
- 05
Fine-tuning assessment
Find out whether LoRA or QLoRA fine-tuning beats prompting or RAG for your task, with evidence.
- 06
Private and open-weight deployments
Llama, Mistral or Qwen models served in your own cloud for strict data control.
Deliverables
Everything you need to run it in production.
- Model and provider evaluation on your tasks
- Secure API integration and key management
- Structured outputs with validation
- Prompt library with versioning and tests
- Caching and routing for cost and latency
- Fine-tuning pipeline when it is justified
- Usage monitoring and budget alerts
Our approach
How we deliver reliable results.
- 1
Benchmark on your data
Shortlisted models are compared on your real tasks for quality, latency and cost.
- 2
Integrate behind a gateway
One controlled layer for keys, logging, rate limits and fallbacks.
- 3
Optimize continuously
Caching, prompt compression and routing are tuned against live usage.
Proof
Related case studies, with real numbers.
Enterprise IT & cloud
Conversational AWS Infrastructure Agent
Natural-language cloud operations on Amazon Bedrock, with confirmation before every change
- CLI commands required
- 0
- CLI commands required
- changes confirmed before execution
- 100%
- changes confirmed before execution
- enterprise model access
- Bedrock
- enterprise model access
Digital health SaaSProduction backend
AI Sponsor
A 24/7 AI recovery-support SaaS with WhatsApp voice and ~90% lower token costs
- lower input-token cost
- ~90%
- lower input-token cost
- text and voice support
- 24/7
- text and voice support
- automated test categories
- 12
- automated test categories
Government & public sector
National Political RAG Assistant
A national-scale AI assistant serving citizens and field teams from one knowledge base
- queries a day, by design
- 10,000+
- queries a day, by design
- audience-specific endpoints
- 2
- audience-specific endpoints
- routing: past vs planned programs
- Temporal
- routing: past vs planned programs
Also delivered
- 15-month Amazon Bedrock integration and user-session management engagement
- Prompt engineering for SaaS and e-commerce teams
Technology
Tools we use for LLM Integration & Optimization.
Model-agnostic and cloud-agnostic: we pick the right components for your data, budget and compliance requirements.
- Claude (Anthropic)
- OpenAI GPT-4o
- Google Gemini
- Meta Llama
- Mistral
- Amazon Bedrock
- Azure OpenAI
- Google Vertex AI
- Hugging Face
- vLLM
- Ollama
- LoRA / QLoRA
Industries we serve
Engagement models
Flexible ways to work, one standard of delivery.
Start small with a discovery sprint or an audit, or go straight to a production build. Most projects start from $5,000, scoped after a free strategy call.
1–2 weeks · fixed scope
AI Strategy & Discovery Sprint
Teams validating an AI opportunity before committing a budget.
- Use-case and ROI workshop
- Data and systems audit
- Architecture blueprint
- Risk and compliance review
- Build plan with estimates
4–12 weeks · milestone-based
Production Build
A defined RAG platform, AI agent, automation or AI product, delivered end to end.
- Architecture and full build
- Evaluation suite and QA
- Security and compliance controls
- Deployment and monitoring
- Documentation and handover
Monthly retainer
Dedicated AI Engineering
Product and platform teams with an ongoing AI roadmap and no time to hire.
- Senior AI engineering capacity
- Weekly planning and demos
- Continuous evaluation and monitoring
- Model and cost optimization
- Scale up with vetted specialists
1–3 weeks · fixed scope
AI Audit & Evaluation
An existing LLM, RAG or agent system that underperforms or is changing hands.
- Architecture and code review
- Answer-quality evaluation
- Security, PII and compliance review
- Cost and latency analysis
- Prioritized remediation report
Not sure which model fits? A free 30-minute call is enough to decide.
Book a free strategy callLLM Integration & Optimization FAQ
Common questions about LLM Integration & Optimization.
Can't find your answer? Ask us on a free strategy call or send a short brief.
Which LLM should we use?
It depends on your task, data sensitivity, latency and budget. We benchmark candidates such as Claude, GPT, Gemini and Llama on your real examples and recommend with numbers.
Can we use LLMs without sending data to a public API?
Yes. Options include Amazon Bedrock or Azure OpenAI within your cloud agreements, or open-weight models hosted entirely in your own environment.
When is fine-tuning worth it?
When you need a consistent style, format or narrow skill that prompting and retrieval can't reach, and you have good training data. We test it against cheaper options first.
How much can prompt caching save?
On workloads with long, stable system prompts, a lot: we cut input-token costs by about 90% on a production SaaS chatbot.
Do you support Amazon Bedrock?
Yes. We have delivered Bedrock integrations, including Claude-based agents and user-session management for production applications.
Taking on new projects
Let’sbuild.
Have an AI project in mind? Let’s make it real.
Book a free 30-minute strategy call with our founder. You’ll leave with a clear view of the right architecture, timeline and next step, whether or not we work together.
- Reply within 24 hours
- NDA before you share anything confidential
- You own the code, prompts and documentation
- Hours overlap with US, UK, EU and Gulf teams