Make engineering knowledge and operational data work as hard as the plant

Quoting, planning, quality, maintenance and traceability depend on technical documents, machine data and engineers’ experience spread across sites and systems. We connect them and put AI where it saves engineering time without loosening control.

Start a conversation The problems we solve

The problems

Where time and margin are lost

  • Production planning by spreadsheet

    Plans are rebuilt by hand whenever orders, materials or capacity change.

  • Slow, inconsistent quoting

    Quotes depend on a few experienced estimators and on past jobs that are hard to find.

  • Scheduling across constraints

    Machines, people and materials are scheduled from partial information.

  • Traceability gaps

    Tracing a batch, part or fault back through the process takes days.

  • Quality data that arrives late

    Quality problems are found after the customer finds them.

  • Technical documentation nobody can search

    Drawings, specifications, manuals and work instructions live in different systems and versions.

  • Engineering knowledge in people’s heads

    How a line really runs is known by a few long-serving engineers.

  • Operational reporting by hand

    Shift, site and performance reports are assembled from several systems.

  • Sensor data with no home

    IoT and machine data is collected but not joined to production and quality data.

  • Sites and acquisitions that differ

    Each site and acquired business runs its own systems and definitions.

  • Supply-chain blind spots

    Supplier performance and inbound materials are tracked by email.

  • Maintenance that reacts

    Maintenance happens after failures because the signals are not used.

Forcing events

When it usually comes to a head

  • A quality failure or recall that exposed traceability gaps
  • An acquisition adding another plant and another set of systems
  • Experienced engineers retiring
  • An ERP or MES replacement
  • Customers demanding faster quotes and better data

What you already hold

The knowledge and data involved

  • Drawings, specifications and bills of materials
  • Work instructions, manuals and runbooks
  • ERP and MES production data
  • Machine, sensor and IoT data
  • Quality records and non-conformances
  • Past quotes, jobs and costs

Workflows

Workflows we change

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

  1. Quoting and estimating

    Quotes drafted from past jobs, costs and specifications for the estimator to check.

  2. Production planning and scheduling

    Plans built on joined-up order, capacity and materials data.

  3. Traceability and quality

    Batches and faults traced through the process in hours, not days.

  4. Engineering documentation and runbooks

    Technical documents and procedures answerable on the shop floor, cited to the page.

  5. Maintenance

    Machine and sensor data used to plan maintenance before failure.

  6. Operational reporting

    Site and performance reporting built on one data model.

Where AI earns its place

AI opportunities

  • AI over engineering documentation

    Engineers ask questions of manuals, specifications and runbooks and get the cited page.

  • Quotes from past jobs

    Estimators start from similar jobs and their real costs.

  • Earlier signals on quality and maintenance

    Sensor and quality data used to spot problems sooner.

  • Captured engineering know-how

    Experience recorded and made answerable before it retires.

What usually sits underneath

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

  • ERP, MES and quality systems that do not share identifiers
  • Documents held in several systems and versions
  • Sensor data collected but not modelled
  • Different systems at each site after acquisitions

Broader technology and data work

  • Data platforms for production, quality and sensor data
  • Integration across sites after acquisitions
  • Independent review of an ERP, MES or IoT programme
  • Technology due diligence on an industrial acquisition

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.

  • A cited assistant over engineering documentation and runbooks
  • A quoting workflow that starts from similar past jobs
  • A traceability model joining production, quality and supplier data
  • A sensor data platform joined to maintenance and quality records

Answerable

Answerable Systems for engineering knowledge

Answerable Systems is set up for engineering knowledge: architecture RFCs, design specifications, runbooks and incident logs. The screen shows it answering an architecture question from internal RFCs, each recommendation cited.

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.

  • Founder’s earlier role

    Engine fleet intelligence at petabyte scale

    For one of the world’s largest aerospace companies, Dan helped build a cloud data platform analysing petabytes of engine data for patterns and anomalies, so maintenance could be predicted early. Delivered in an earlier role.

    See earlier work
  • Demonstration configuration

    Answerable Systems: Engineering & Cloud

    A configuration for runbooks, architecture decisions and technical documentation, 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

Start with the knowledge your engineers search for most

We will look at where it lives, what it is worth and what it would take to make it answerable on the shop floor.

Start a conversation See Answerable Systems