AI & Machine Learning

Apply AI to business information and workflows with clear evaluation and human oversight.

AI experiments stall because answers are untrusted, permissions are unclear, or nobody owns evaluation.

We build grounded assistants, document extraction, forecasting, and AI-assisted workflows against approved sources—with evaluation, permission design, cost assumptions, and human review for consequential actions. Neural networks are used only where data, task, and evaluation justify them—not as a seventh service line.

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How this work looks

AI & ML covers grounded answers and agent workflows with human approval—see Apply AI below.

Connect → Understand → Apply AI → Act
Interactive demo · Sample data · Simulated AI
3/4
RAG · Answers grounded in your business knowledgeAgentic workflow · Human approval
RAG · Q&A
“What do our sales notes say about delayed deals in the West region?”
  1. 1Question received
  2. 2Search knowledge
  3. 3Retrieve excerpts
  4. 4Answer with sources

Waiting for question…

Building answer from excerpts…

Agent pipeline
Human-in-the-loop

Trigger

Delayed opportunity OPP-3310

Apex Manufacturing · West · $48k

  1. Classify risk

    High · legal / procurement hold

  2. Pull CRM notes

    CRM

    MSA addendum blocking close

  3. Draft follow-up

    Email to procurement + legal

  4. Request approval

    Approve

    M. Rivera · West sales

  5. Update opportunity

    CRM

    Status → In review

Ask questions. Get grounded answers—and agent workflows with human approval.

What’s included

AI Strategy & Implementation

Use-case specification, feasibility, and delivery against approved sources—with evaluation and oversight designed in.

AI Assistants & Enterprise Search

Q&A over approved repositories with citations, scoped access, and clear boundaries.

Document Intelligence

Extraction, classification, and summarization from approved document sets with human review where decisions matter.

Predictive Analytics & Machine Learning

Forecasting and anomaly detection evaluated against agreed criteria—illustrations are never presented as trained production models.

Microsoft Copilot & Copilot Studio

Copilot and Copilot Studio implementations grounded in approved data—no partnership claim implied.

Model evaluation, monitoring & improvement

Baselines, evaluation sets, and operating cost assumptions—not a model-provider partnership pitch.

Example use cases

Hypothetical patterns · Not customer claims

Policy and SOP search

Help staff find approved documents with visible evidence—not open chat over the whole company.

Exception prioritization

Surface which records need review first, with confidence labeled honestly.

Assisted intake

Draft enrichment for a human reviewer before any customer or financial update.

What engagement includes

  • Use-case specification against approved data and risk posture
  • Baseline and evaluation criteria
  • Prototype or deployed workflow
  • Permission design and human-review process
  • Cost assumptions and handover

What we ask from you

  • Approved data sources and access constraints
  • An evaluation owner
  • Examples of the questions or workflows that matter

Sample deliverables

  • Use-case specification
  • Baseline
  • Prototype or deployed workflow
  • Evaluation dataset and results
  • Permission design
  • Cost assumptions
  • Human-review process

Engagement boundaries

  • Demo animations are UI illustrations—not trained neural networks.
  • We do not sell standalone cybersecurity or phishing-awareness programs.

Relevant platforms: Power BI · Microsoft Fabric · Palantir Foundry

Technologies we work with

Confirmed capabilities only — not certifications or partnership badges.

  • Power BI
  • Microsoft Fabric
  • Palantir Foundry
  • SQL
  • dbt
  • Python
  • Power Apps
  • Power Automate
  • Azure Data services
  • Databricks

FAQs

Not for consequential actions. We design for human review where outcomes affect customers, money, or permissions.

Ready to talk through this capability?

Tell us about your situation. We will help you define a practical next step.

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