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DaVinci AI
Services · Applied AI

AI where it actually fits.

LLM workflows, document understanding and intelligent automation, applied to the work that actually slows your team down.

LLM workflows

Workflows, not chatbots.

The interesting use cases for LLMs are rarely “a chat window.” They’re the orchestrated workflows behind a button, drafting a response, summarizing a thread, classifying an exception, surfacing the three relevant policies. We design those workflows with retrieval, evaluation and guardrails built in.
  • Retrieval-augmented generation (RAG) pipelines
  • Structured generation with schema enforcement
  • Evaluation harnesses and regression suites
Document intelligence

Make unstructured data behave like structured data.

Contracts, forms, PDFs, scanned records, most organizations sit on a mountain of unstructured data that should be feeding their analytics. We build document-understanding pipelines that extract, classify and cross-reference at scale, with confidence scores you can act on.
  • OCR, layout-aware extraction and classification
  • Entity normalization and cross-record linkage
  • Human-in-the-loop review tooling
Responsible deployment

Designed for the regulator in the room.

In regulated industries, “the model said so” is not an answer. We bake in evaluation, audit trails, sensitive-data handling and explainability from day one, not as an afterthought when compliance asks.
  • Data residency and PII handling
  • Audit logging and decision traceability
  • Bias, fairness and red-team evaluations
AI capability surface

What applied AI looks like in practice.

Less “what could AI do?” and more “what does it ship?” The patterns we keep deploying for clients.

Knowledge retrieval

  • RAG over policy and knowledge bases
  • Semantic search over internal docs
  • Source-grounded Q&A with citations
  • Retrieval evaluation harnesses

Drafting & summarization

  • Response and email drafting
  • Meeting and call summarization
  • Report and brief generation
  • Tone, style and brand controls

Classification & routing

  • Inbox triage and case routing
  • Document type classification
  • Intent detection
  • Severity and priority scoring

Document understanding

  • Layout-aware extraction (PDFs, forms)
  • Table extraction and normalization
  • Entity linking and dedup
  • Contract clause analysis

Agentic workflows

  • Tool-using assistants with guardrails
  • Workflow orchestration with checkpoints
  • Human-in-the-loop approvals
  • Long-running task management

Evaluation & guardrails

  • Regression and rubric-based evals
  • Hallucination and grounding checks
  • PII and policy filters
  • Red-team test suites
Case snapshot

How it plays out, in practice.

A representative engagement, described in the structure of challenge, approach and outcome. Specifics changed to preserve client confidentiality.

Procurement Spend Intelligence
Public Sector

Procurement Spend Intelligence

Challenge

A program office had millions of procurement records, invoices, POs, contracts, most of them unstructured. Spend was being managed at the line-item level but never aggregated across vendors and business units.

Approach

  • Built a layout-aware extraction pipeline for invoices, POs and contracts
  • Normalized vendor names and item descriptions into a clean taxonomy
  • Surfaced duplicate-spend patterns and consolidation opportunities with confidence scores
  • Designed a human-in-the-loop review queue so analysts could validate and refine

Outcome

Within a quarter, the program had identified material consolidation opportunities across vendors that had previously appeared unrelated, and a sustainable workflow for the analyst team to keep finding them.

How we partner

Three formats. All senior-led.

Most engagements start with a Discovery sprint, then graduate to a Build sprint or Embedded team. We’re happy to start anywhere that fits the work.

012–4 weeks

Discovery sprint

A focused engagement to define the decision worth informing and prove the data exists to inform it. Ends in a working prototype, an honest feasibility read, and a costed roadmap.

Typical deliverables

  • Decision and KPI map
  • Data feasibility assessment
  • Working prototype on your data
  • Costed roadmap to production
028–12 weeks

Build sprint

A senior pod takes a defined initiative from prototype to production-grade system, designed for your stack, instrumented for adoption, hardened for the real world.

Typical deliverables

  • Production-grade build
  • CI/CD, monitoring and runbooks
  • Stakeholder training and enablement
  • Ninety-day adoption review
03Quarterly

Embedded team

For organizations standing up an internal capability, we embed alongside your team, shipping production work while transferring practice, patterns and ownership.

Typical deliverables

  • Quarterly outcomes plan
  • Pair-building and code review
  • Standards, templates and playbooks
  • Capability transfer and handoff
Frequently asked

Questions we hear, answered honestly.

Which models do you build on?
Mostly frontier models from OpenAI, Anthropic and Google, and open models (Llama, Mistral, Qwen) when data residency, cost or latency demand it. We’re model-agnostic by design; the evaluation harness is the thing we hold steady.
How do you prevent hallucinations?
You don’t prevent hallucinations, you design for them. Grounded retrieval, structured generation with schema enforcement, citation requirements, calibrated abstention, and evaluation harnesses that catch regressions before they reach users. We treat every LLM output as untrusted input until it’s been validated.
Can our data stay in our tenant?
Yes. We deploy AI workloads inside your Azure, AWS or GCP tenant where that’s the policy. For frontier models, we use the enterprise-grade endpoints (Azure OpenAI, AWS Bedrock, Vertex) that don’t train on your data.
What about cost?
Modeled per-use-case from day one. We pair build with a cost dashboard so the bill doesn’t become a surprise, and we routinely cut cost significantly through better prompts, caching, smaller models for cheap paths, and only escalating to frontier models when warranted.
How do you handle PII?
PII detection, masking and tokenization before model calls; audit logging of prompts and outputs; tenant-local deployments where regulation demands. We’ll walk you through the threat model and controls in detail during scoping.
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Have a problem worth solving?

Whether you’re scoping a new initiative, modernizing analytics, or evaluating where AI actually fits, we’d be glad to talk.

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