Generative AI, grounded in your business.
Large language models can draft, summarize, search and reason — but enterprise value comes from grounding them in your own knowledge, securing them properly and measuring the results. That is what we build.
Overview
We design and deliver generative-AI applications on leading foundation models, connected to your documents, databases and business systems through retrieval-augmented generation (RAG). Answers cite their sources, respect user permissions and stay inside your security boundary.
Every solution ships with evaluation: test sets, quality and safety metrics, cost tracking and human feedback loops, so you can prove accuracy before rollout and keep improving after it.
What you get with us.
AI assistants & copilots
Internal and customer-facing assistants for support, sales, HR and IT.
RAG & enterprise search
Ask questions across policies, contracts and knowledge bases with cited answers.
Document automation
Extraction, summarization and drafting for invoices, claims, contracts and reports.
Prompt & model engineering
Prompt design, fine-tuning and model selection balanced for quality, speed and cost.
Evaluation & guardrails
Automated evals, hallucination checks, PII filtering and content safety.
Secure deployment
Private networking, data residency, access control and audit logging.
Model & platform options
We choose the right model for each task and avoid lock-in.
- Amazon Bedrock
- Azure OpenAI Service
- Google Vertex AI (Gemini)
- Anthropic Claude & OpenAI models
- Open-weight models (Llama, Mistral)
- Vector databases & search (pgvector, OpenSearch, Azure AI Search)
A clear path, phase by phase.
A proven, phased approach. Timings are typical and depend on your scope, data and systems.
Discover
Use-case workshop with business owners; score ideas on value, feasibility and risk; pick one or two to start.
Prototype
Build a working version on real (approved) data with your users, not a slide deck.
Evaluate
Create test sets, measure accuracy, safety and cost, and add guardrails before anyone relies on it.
Productionise
Security review, identity, integration, monitoring, LLMOps pipelines and support model.
Scale
Reuse the platform, prompts and evaluations for the next use cases; drive adoption.
Ways to work with us on Generative AI.
GenAI Discovery
Identify and prioritise your best generative-AI opportunities.
- Stakeholder workshops
- Use-case scoring & value cases
- Data & risk assessment
- Roadmap and next steps
GenAI Pilot
Prove value on one use case with real users and real data.
- Working prototype
- Evaluation set & quality report
- Security & cost review
- Go / no-go recommendation
GenAI in Production
Take a proven pilot live and keep it improving.
- Production architecture
- Guardrails & monitoring
- LLMOps & CI/CD for prompts
- Training & handover
Where Generative AI delivers value.
Knowledge assistants
Answers from policies, manuals and wikis — with citations.
Customer-service copilots
Suggested replies and summaries for support agents.
Document processing
Extract, classify and summarise contracts, invoices and claims.
Content generation
Product, marketing and report drafts with human review.
Analytics Q&A
Ask questions of your data in plain language.
Meeting & case summaries
Turn calls, meetings and case notes into actions.
Translation & localisation
Consistent multilingual content at scale.
Developer assistants
Code explanation, tests and documentation support.
The stack we work with.
Vendor-neutral: we choose tools to fit your environment.
Models
Platforms
Retrieval & data
LLMOps & safety
Generative AI: common questions
How long does it take to get a generative AI use case into production?
A focused pilot typically takes a few weeks; moving a proven pilot into production usually takes several more, depending on integrations, security reviews and data readiness.
Is our data safe with generative AI?
It can be. We deploy models inside your cloud account where possible, use private networking and existing identity controls, filter sensitive data and choose services whose terms exclude training on your data.
Which model should we use?
It depends on the task, quality bar, latency and cost. We test a few candidate models on your own examples and keep the architecture model-agnostic so you can switch later.
How do you control generative AI costs?
By choosing the smallest model that meets the quality bar, keeping prompts and context lean, caching, batching non-urgent work and monitoring usage per application.
Generative AI in practice
A GenAI knowledge assistant for a consulting firm
How retrieval-augmented generation could let consultants find past proposals, methods and expertise in seconds.
Read moreAutomating freight documents with generative AI
How a freight forwarder could extract and validate data from bills of lading, invoices and customs documents.
Read moreAgentic AI for insurance claims intake
How an AI agent could triage first notice of loss, check completeness and route claims — with adjusters in control.
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Learn moreReady to build what’s next?
Tell us about your project. Our consultants in Florida and Hyderabad will get back to you within one business day.