LLMs that work foryour business, notjust demos.
From knowledge assistants and document intelligence to customer-facing AI, we design, build and integrate LLM solutions around your data, workflows and business goals.
- Bedrock & Bedrock Agents
- AWSBedrock & Bedrock Agents
- Grounded in your data
- RAGGrounded in your data
- Typical engagement
- 6-14wksTypical engagement
Impressive demos todependable business tools.
A compelling AI demo is easy. Making it accurate, useful and reliable enough for real users is harder.
We engineer the systems around your LLM — connecting it to your business data, designing the right retrieval and prompting architecture, evaluating its outputs and putting the necessary controls around it. The result is an AI capability designed for the real world, not a prototype waiting for production.
What you get
with every engagement.
- Data readiness assessment and gap analysis
- Feature store design and implementation
- Experiment tracking setup (MLflow / SageMaker Experiments)
- Production model deployment (API, batch, or embedded)
- CI/CD pipeline for model versioning and promotion
- Automated retraining pipeline with drift detection
How we run this
Engagement.
1. Use Case Definition
We scope the specific problem the LLM will solve, define success metrics (accuracy, latency, cost per query) and identify the data sources that will ground it.
2. Plan & Design
We choose the right foundation model and inference platform, design the RAG pipeline or fine-tuning strategy, and build the data ingestion and embedding infrastructure.
3. Migrate & Validate
We build the system, run rigorous evaluations across accuracy, hallucination rate, latency and cost, and iterate until it meets your quality bar.
4. Optimise & Handover
We integrate the LLM feature into your product or internal tooling, add observability and safety controls, and support launch and post-live monitoring.
What you'll walk away with.
Production-ready LLM feature
A shipped, integrated AI feature in your product — not a demo that never leaves the sandbox.
Outputs grounded in your data
RAG architecture ensures the model answers from your content, reducing hallucination and improving accuracy.
Measurable efficiency gains
Quantifiable reduction in manual knowledge work — support tickets, document review, report generation.
Safe and observable
Safety guardrails, content filtering and full observability so you know exactly what the model is saying and why.
Ready to talk about
Generative ai & LLMs integration?
Fixed-price projects or ongoing managed service retainers — no lock-in, no hidden fees. Talk to a cloud architect today.