AI & Machine Learning

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

Assistants, document extraction, and forecasts on approved sources, with evaluation and a person reviewing consequential actions.

Part of AI Automation & Business Applications

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AI & Machine Learning

Build AI assistants, document search, forecasting, and anomaly detection around your business. We connect models to relevant data and evaluate their outputs for practical, dependable use.

Grounded assistants & document searchForecasting & anomaly detectionEvaluated AI-assisted workflows

The problem, and how we fix it

Where it gets stuck

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

How we fix it: 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. Develop predictive models and neural-network solutions, with evaluation and monitoring built into delivery.

Illustrative example

Who it's for

Businesses with a specific, repeatable task in mind, such as reading documents, answering questions from approved files, or forecasting demand, that want it tested before it is trusted.

Typical starting points

  • Grounded assistants & document search
  • Forecasting & anomaly detection
  • Evaluated AI-assisted workflows

What's included

Build AI assistants, document search, forecasting, and anomaly detection around your business. We connect models to relevant data and evaluate their outputs for practical, dependable use.

  • 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.

Put your business knowledge and data to work.

We build AI assistants, agents, and predictive solutions with relevant context, source references, evaluation, and appropriate human oversight.

Illustrative example. A maintenance company wants help investigating repeat equipment failures and preparing follow-up work.

Sample scenarioNorthspan MaintenanceFictional pump history, not a client asset

Paused · Step 1 of 4

Question

What should we check before servicing Pump P-104?

Context

Answer

  1. Confirm isolation and lockout.
  2. Inspect the seal housing for the leak noted on 18 Feb 2026.
  3. Confirm the replacement seal product code before install.
  4. Do not estimate a bearing replacement date. It is not in the retrieved history.

Proposed action

Draft work request WR-204 · Pump P-104 · inspection checklist attached.

Approving records the decision in this sample. It does not dispatch a technician.

  • No work request recorded yet. This sample does not contact an external system.

Answers supported by sources, useful predictions, and controlled actions.

Platforms and tools we work with

  • Palantir Foundry
  • Microsoft Copilot Studio
  • Azure AI

Capabilities

  • Answers with sources
  • Your business context
  • A suggested next step
  • Checked results
  • A person approves

Selection depends on the client environment. Not every implementation uses every tool. Not a partnership or certification list.

Tell us about this workflow. We'll follow up within 2 business days.

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Platforms we work with

We use these platforms when they fit your systems. Named so you know what we can support, not as certifications or partnerships.

Example use cases

Illustrative 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.

Deliverables and expectations

What you leave with

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

What this does not cover

  • Demo animations are UI illustrations, not trained neural networks.
  • Security work, including email and phishing protection, is covered by our separate Cybersecurity service.

What you can count on

  • 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

How an engagement runs

Every engagement starts with a conversation, then a clear written scope.

  1. Understand

    We look at how the work happens today and agree on the one problem to solve first.

  2. Agree the scope

    You get a short written plan: what changes, what stays, and how we will check it worked.

  3. Build and check

    We build in small steps and review each one with the people who will use it.

  4. Hand over

    Your team gets plain notes and a walkthrough, so they can run it without us.

Tools we use

Named so you know what we can work with. These are tools, not the product, and not partnership badges.

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

FAQs

Will AI make consequential changes unsupervised?

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

Are you partnered with a specific model provider?

No partnership claim is implied. We implement against the platforms and policies that fit your environment.

Related solutions

Tell us which task you want AI to help with.

Contact us for a Free Assessment. We'll follow up within 2 business days.

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