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AI for engineering firms

AI for engineering firms. Decades of work, searchable in seconds.

Private knowledge systems and custom AI agents that let your team find past calculations, reports and procedures instantly, with sources, without interrupting the people who wrote them.

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Where the time goes

Every practice has the same problem: the answer was produced once, filed under a name only one person would think of.

01

Calculations and reports going back fifteen years, filed differently by every engineer who wrote them

02

Graduates interrupt directors with questions the archive has already answered

03

New engineers take months to learn which template, standard and colleague to go to

04

Proposals reuse nothing, because finding the comparable job takes longer than starting again

How we solve it

We index your archive, keep the permissions you already set, and link every answer back to the file it came from so your team can check the evidence.

Custom RAG & domain LLMs

Ask the archive a question

Search in plain English for a load case, a detail, a procedure or a past project, and get the file, section and page behind the answer.

Custom RAG & domain LLMs

Permissions carried over, not rebuilt

Access follows what is already set on your folders and systems, so commercially sensitive jobs stay out of reach for people who cannot open them today.

Custom RAG & domain LLMs

Onboarding that runs itself

New colleagues find their own answers on the first project instead of waiting for a free hour from a director.

Custom AI systems & agents

Less admin around report production

Pull data, checklists and figures from your systems into the report structure your practice already uses, leaving the engineer to review the work.

Custom RAG & domain LLMs

Comparable jobs for proposals

Retrieve the scopes, rates and programme notes from similar past work so a first draft starts from evidence rather than memory.

Project stories

What this could look like in your business.

Structural engineers · Cheshire

Past expertise, without another interruption.

An engineering practice with calculations and reports going back to 2011, filed differently by every engineer who wrote them. Graduates keep interrupting directors with the same questions.

A private knowledge assistant now indexes the archive, keeps the existing folder permissions and links every answer straight back to the source file it came from.

Older scanned drawings still give patchy results in places. The team reviewed retrieval quality together before relying on it for anything that matters.

Directors get fewer interruptions, and new colleagues find their own answers from day one.

What to measure

Agree the targets. Test the improvement.

These are measures for a pilot, not claimed client results. We agree your starting point and targets during discovery, then review the evidence with your team.

Time to find a past calculation

Minutes from a question to the file that answers it, with the source shown.

Interruptions to senior staff

How many routine questions get answered without a director being asked.

Time to get a new engineer productive

How quickly someone new contributes without a colleague carrying them.

Time to prepare a proposal

Hours saved assembling scope, rates and references from comparable work.

Systems we connect
SharePointWindows file serversAutodeskBluebeamExcelProcoreGoogle WorkspaceAnything with an API

We build around the tools you already run. Anything with an API or a database is fair game, with access and scope agreed during discovery.

Who this is for

Best suited to practices where written work is the product and being wrong about a figure carries real consequences.

  • Structural and civil engineering consultancies
  • MEP and building services practices
  • Mechanical, process and environmental engineering firms
  • Technical directors carrying the risk of a wrong answer reaching a client
How we work

Discover, build, evaluate, operate.

01

Discover

Start with the task taking too much time. We look at the data, the systems and the constraints, then agree a starting point and the measures that show whether it worked.

02

Build

We build a focused version on one high-value problem, with the boundaries, permissions and approval points your team needs.

03

Evaluate

Your team compares the result against the agreed starting point. Gaps and teething problems are reviewed together before a wider rollout.

04

Operate

We monitor quality, cost and drift, then extend the system where it earns its place in the workflow.

FAQs

Questions, answered.

What the system does, what stays with your team, and how your data is handled.

Related
Private knowledge systemsPrivate AI document searchEngineering outcomesBook a discovery call

What does your team keep asking each other?

Bring the archive and the recurring questions. We will show how well it answers before you decide on a rollout.

Discuss your bottleneck Book a discovery call