Our AI Solutions
We design, build and run retrieval-augmented generation (RAG), natural-language querying (NLQ) and lightweight agents using Azure AI Foundry and Snowflake Cortex. Everything is delivered with governance, evaluation and cost control - so you can scale from pilot to production with confidence.
What we're great at building alongside you​r team
1. Knowledge search & Q&A (RAG)
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Permission-aware answers with citations over SharePoint, OneLake/ADLS, Snowflake and line-of-business content. Tuned chunking, embeddings and re-ranking for accuracy and speed.
2. Natural Language Query (NLQ) & "Conversational analytics"
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Ask plain English questions and receive validated charts and narratives over Foundry and Fabric semantic models or Snowflake metrics — with synonyms, row-level security and guardrails to prevent unsafe queries.
3. Document automation
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Classify, extract and summarise content (cases, claims, requests). Use Azure AI Document Intelligence (Form Recogniser) and LLMs to draft responses, route work and log actions for audit.
4. Decision support & workflow agents
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Agentic flows that call APIs, check policies, orchestrate approvals and hand off to humans when needed—using Prompt Flow, Functions/Logic Apps or Databricks Workflows.
5. Predictive & traditional ML (where it fits)
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Time-series, propensity and classification models integrated into RAG/agent workflows for better recommendations and triage.
How we deliver (safe, measurable, fast)
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Use-case design & business case: Value framing, risks, success measures and adoption plan (AI Use-Case Canvas).
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Data & access readiness: Source mapping, sensitivity labels, entitlement alignment, PII redaction.
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Architecture & build: Reference patterns for RAG/NLQ/agents; secure endpoints, feature stores, vector indices.
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Evaluation & safety: Offline/online tests, golden sets, hallucination/PII checks, toxicity filters, human-in-the-loop.
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Operate & optimise: Telemetry, error triage, rollback, cost/latency budgets, capacity planning and FinOps.
What we use:
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Azure
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AI Foundry (Model Catalogue, Prompt Flow, evaluation)
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Azure AI Search (hybrid retrieval, vector + keyword)
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LLMs: Azure OpenAI (and approved OSS models)
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Data estate: Fabric/Synapse, Databricks, ADLS/OneLake
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Security: Entra ID, Private Link/VNET, Key Vault, Defender for Cloud, Purview for catalogue/lineage
Snowflake
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Cortex (Search, Functions) with native vector capabilities
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Snowpark for Python/ML, secure UDFs and pipelines