From AI product idea to a platform that scales

Software and technology businesses need AI in the product, data customers can trust and platforms that keep up with growth. We bring product thinking, architecture and hands-on engineering to design, build, productionise and modernise.

Start a conversation The problems we solve

The problems

What slows technology businesses down

  • Product strategy for AI

    Pressure to add AI to the product without a clear view of what customers will pay for.

  • AI product design

    Features that demo well but fail on real customer data, edge cases or cost.

  • Data products

    Valuable data that could be a product, held in a shape customers cannot use.

  • Prototype to production

    Pilots that never reach production because evaluation, security and cost were left until last.

  • Scaling

    Architecture that worked for the first customers strains under growth.

  • Platform modernisation

    Legacy services, middleware and data stores slow every release.

  • Product telemetry

    Usage data collected but not turned into product decisions.

  • Customer knowledge and support

    Support teams answer from documentation, tickets and engineers’ memory.

  • Acquisition and integration

    Acquired products bring different stacks, data and teams.

  • Engineering knowledge

    Architecture decisions and runbooks are hard to find when they matter.

Forcing events

When it usually comes to a head

  • Investors or the board asking for an AI roadmap
  • A competitor shipping an AI feature
  • Infrastructure cost rising faster than revenue
  • A funding round, acquisition or due diligence
  • Enterprise customers asking about AI security and data handling

What you already hold

The knowledge and data involved

  • Product documentation and help content
  • Support tickets and resolutions
  • Product telemetry and usage data
  • Architecture decisions, runbooks and incident reviews
  • Customer contracts and requirements
  • Code repositories and technical specifications

Workflows

Workflows we change

Specific pieces of recurring work, each with an owner, a baseline and a measure of what changed.

  1. AI features in the product

    Designed, evaluated and costed before launch, with guardrails and monitoring.

  2. Support and customer knowledge

    Support answers grounded in documentation and resolved tickets, cited.

  3. Engineering knowledge

    Runbooks, architecture decisions and incident history answerable for engineers.

  4. Product analytics

    Telemetry turned into the product decisions it was collected for.

  5. Release and operations

    Evaluation and monitoring that make AI features safe to change.

Where AI earns its place

AI opportunities

  • AI product features that survive production

    Designed with evaluation, cost and guardrails from the start.

  • Support that answers from the product’s own knowledge

    Consistent, cited answers for support teams and customers.

  • Engineering knowledge on hand

    Engineers find the decision, the runbook or the past incident in seconds.

  • Data products

    The data the business holds, shaped into something customers will pay for.

What usually sits underneath

When AI disappoints, the cause is usually the data, systems and ownership beneath it, so we look there too.

  • Middleware and services added faster than they were designed
  • Data stores that grew with each feature
  • No evaluation or monitoring for AI behaviour
  • Cloud cost without clear ownership

Broader technology and data work

  • Architecture and platform modernisation
  • Data platforms and lakehouses
  • Technology due diligence for investors and acquirers
  • Fractional CTO or head of data leadership

What we could build or change

Examples of the work

Illustrations of what an engagement could produce, scoped to your own information and measured against a baseline.

  • An AI feature taken from prototype to production with evaluation and monitoring
  • A support assistant grounded in documentation and resolved tickets
  • An engineering knowledge assistant over runbooks and decisions
  • A platform rebuilt around a lakehouse to cut latency and cost

Answerable

Answerable Systems and Assist

Systems makes engineering knowledge answerable for the people who run the platform: RFCs, specifications, runbooks and incident logs. Assist does the same for support teams. Both are shown on real screens from demonstration environments.

See every configuration

Answerable Systems: Engineering & Cloud

Set up for engineering teams: architecture RFCs, design specifications, API documentation, runbooks and incident logs.

Answerable Systems screen with an architecture answer, a recommended-standards table and two numbered sources (opens the screen full size in a new tab)
Answerable Systems answering an architecture question from internal RFCs, each recommendation cited. Real product screens from demonstration environments. Organisation and author names in them are fictional, and the figures show how the product answers, not findings.

Proof

What we have built and done

Each item says where it comes from: TechGuidr’s own products, the founder’s earlier roles or demonstration configurations.

  • TechGuidr product

    Two AI products built from scratch

    TechGuidr conceived, designed, architected and built Answerable and Bearing: the product thinking, architecture, engineering, data foundations, AI implementation and delivery. Both are in production.

    Read the case study
  • Founder’s earlier role

    A SaaS platform and its iOS and Android apps, rebuilt

    Delivered by Dan in an earlier technology leadership role: the SaaS application and its mobile apps rebuilt around a Databricks lakehouse, with AI performance coaches embedded. Dashboard queries went from four to five seconds to under 600 milliseconds.

    Read the case study
  • Demonstration configuration

    Answerable Systems and Assist

    Configurations for engineering knowledge and customer support, shown on illustrative content.

    See the configurations

How we engage

Where to start

Every engagement starts from the decision or outcome you need. Prices are starting points and exclude VAT.

Industries

Related industries

All industries

Bring the feature, the platform or the cost that worries you

We will tell you what it would take to make it work in production, and what it should cost to run.

Start a conversation See Build & Productionise