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AI and Machine Learning

Applied AI, predictive models and automation, chosen for problems where they clearly pay for themselves.

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Proof of value
4–6 weeks on your data
Use cases
Assistants, documents, prediction, automation
Models
OpenAI, Anthropic, open-source, classic ML
Guardrails
Privacy, testing and human review

Overview

Applied AI, Models and Automation That Pay for Themselves.

AI is useful when it solves a specific, measurable problem: answering routine questions, reading documents, forecasting demand or flagging risk. We start there.

We help you pick the right use cases, build and test models on your data, and put them into production with the monitoring they need.

You Might Need This If…

  • Staff spend hours reading, summarizing or re-keying documents.
  • You have pilots that never made it into production.
  • Customers wait for answers that already exist in your knowledge base.
  • You want to forecast demand, churn or risk but rely on gut feel.
  • Leadership wants an AI plan grounded in real use cases and costs.

What we deliver

AI and Machine Learning Services

Take one piece or the whole scope, delivered by one team.

  • Use-Case Discovery

    Workshops to find where AI would save time or money, and what data it needs.

  • Generative AI Assistants

    Assistants and search over your own documents and knowledge, with controls on what they can access.

  • Document Processing

    Extracting data from invoices, forms and contracts into your systems.

  • Predictive Models

    Forecasting, scoring and classification models built on your data.

  • Process Automation

    Automating repetitive tasks across systems, with people kept in the loop.

  • Model Operations

    Deployment, monitoring and retraining so models stay accurate.

What good looks like

The Outcomes We Work Towards

We agree how success will be measured before work starts, and report against it.

  • Use cases with a business caseEach one sized for value, cost and risk before building.
  • Proven on your dataA working prototype measured against your own examples.
  • Safe in productionMonitoring, evaluation and human review where it matters.

How we work

Four Phases, with Clear Deliverables

Each phase ends with something you can review and sign off, so you can stop, change course or continue with confidence.

  1. 12 weeks

    Pick the Use Case

    Agree the problem, the data and how success will be measured.

    You get

    • Use-case shortlist
    • Value and feasibility scores
    • Data readiness check
  2. 24–6 weeks

    Prove It

    A short proof of concept on real data before any larger commitment.

    You get

    • Working prototype
    • Accuracy measured on your data
    • Cost per transaction
  3. 36–12 weeks

    Productionize

    Integrate with your systems, with security and access controls.

    You get

    • Production integration
    • Security and privacy review
    • User training
  4. 4Ongoing

    Monitor and Improve

    Track accuracy and cost, and retrain as data changes.

    You get

    • Quality monitoring
    • Model and prompt updates
    • Usage and cost reports

Technology

Tools and Platforms We Work With

We choose tools to fit your stack and your team, and we document what we build so you’re never locked in.

  • Python
  • Azure AI
  • AWS AI services
  • Large language models
  • Vector search
  • MLflow
  • Power Automate

You see progress the whole way through, in the same kind of view shown here.

Examples

What This Work Looks Like

Two typical engagements: the problem, what we did, the team and the result.

Example scenario · Logistics and freight business

Reading Invoices and Claims Automatically

The situation

A team keyed data from thousands of invoices and delivery notes in different formats each month.

What we did

  • Built a document pipeline that extracts and checks key fields
  • Sent low-confidence items to people for review
  • Posted approved data straight into the finance system
Team
AI engineer, data engineer, integration developer, analyst
Timeline
3 months from prototype to production
Result
Most documents now pass straight through, with people checking the exceptions.

Example scenario · Telecom provider

An Assistant for Customer Service Agents

The situation

Agents searched several knowledge bases while customers waited on the line.

What we did

  • Indexed product, policy and troubleshooting content
  • Built an assistant that drafts answers with links to sources
  • Tested answers with agents before rollout
Team
AI engineer, 2 developers, QA engineer, product owner
Timeline
4 months
Result
Agents find answers faster, and every suggestion shows its source.

These examples show the kind of work we do. They are illustrative and do not describe a named client.

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Canada edition

AI in Canada

  • Privacy impact assessments for models that use personal information.
  • Transparency for automated decisions, as Québec’s Law 25 requires.
  • Model risk practices aligned with OSFI guidance for federally regulated lenders.
  • A Toronto-based delivery lead on every project, with engineering in India for extended hours.

Team and pricing

Who You Work with, and How It’s Priced

Every engagement has a named delivery lead. Need individuals rather than a team? We place machine learning engineers, data scientists, AI engineers and more on contract, contract-to-hire or direct hire terms.

A Typical Team

Sized to the work; this is a common starting point.

  • AI/ML engineer1–2
  • Data engineer1
  • Software developer1–2
  • Data scientistAs needed
  • Delivery lead1

Project

A fixed scope and timeline, led by our delivery lead. Often fixed price for discovery and first release.

Dedicated Team

A stable team working on your backlog and tools, billed monthly. Scale up or down with notice.

Managed Service

We run and improve what has been built, against agreed service levels, for a monthly fee.

Hire machine learning engineers instead

FAQs

Questions We Often Hear

Is our data safe with AI tools?

We use enterprise model services with no training on your data, keep data in your cloud where possible, and review privacy before anything goes live.

Which model do you use?

Whichever performs best for the task and cost. We test several and can switch later without rebuilding.

How do you measure accuracy?

Against a test set of your own examples, agreed with you before we start, and re-run every time something changes.

What if the AI gets it wrong?

We design for it: confidence thresholds, human review for exceptions and clear sources for every answer.

Where should we start?

With a short discovery to shortlist two or three use cases, then a four to six week proof of value on the strongest one.

Industries

Industries We Deliver For

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Next step

Talk to Us About AI and Machine Learning

Tell us what you need and when. We’ll come back with an approach, a team and a plan.

  1. We reply within one working day.
  2. A short call with someone who knows the subject, not a salesperson reading a script.
  3. A written proposal or shortlist, usually within a week.

Prefer email? Write to info@tekishub.com.

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