AI for Engineering Firms

It depends on scope and integration depth. A focused single-workflow build (e.g. tender automation or drawing retrieval) typically runs $30,000–$80,000; a broader rollout across multiple workflows with deep EDMS/project-system integration and firm-wide review controls runs $80,000–$200,000+. We almost always start with a fixed-price PoC (typically $15,000–$25,000) that proves the hours saved on one workflow before you commit to more. Every engagement begins with a free discovery call to scope requirements and give you a realistic estimate — and an honest view of the likely ROI.

A focused PoC delivers a working tool on your real project data in 4–6 weeks, with measured hours-saved on the target workflow. A production rollout of a single high-value capability typically takes 10–16 weeks including integration, review controls, and adoption support. The biggest variable is how accessible and well-organised your drawings and documents are — a tidy EDMS makes integration fast, while scanned legacy archives add preparation time. We sequence delivery so you see working software and an early proof of value, not a long silence before a big-bang launch.

Accuracy and clear human ownership are the price of admission, so we design for them at several layers. AI drafts, retrieves, and checks — but a qualified engineer reviews and owns anything carrying liability; the stamp stays human, enforced by the workflow rather than left optional. Every output is grounded in your own source documents and codes with citations, so an engineer can verify against the clause instead of trusting a black box; structured outputs are validated, low-confidence results are flagged and escalated, and AI-generated content is kept visibly distinct from engineer-checked content with an audit trail of who reviewed what. We agree an accuracy benchmark up front and monitor for drift in production. The aim isn't a system that's never wrong — it's one whose output is always verifiable and always checked by a qualified engineer before it carries weight.

No — and any vendor pitching AI that 'makes engineering decisions' should worry you. We build AI as an accelerator of your engineers' judgment: it does the searching, first-drafting, and checking so your people spend their time on design, decisions, and the work that carries their stamp. A qualified engineer always reviews and owns liability-bearing output. The goal is to give your engineers more capacity for high-value work and to capture firm knowledge — not to remove the human expertise that is the firm's actual value.

Yes — and handling real engineering documents and systems is exactly where generic AI tools fall down. We process CAD files, BIM data, scanned legacy drawings, marked-up PDFs, and long specifications with the right parsing for each, not naive text extraction that loses the engineering content. And we connect to the systems your projects live in — document control and EDMS (Aconex, ProjectWise, SharePoint), CAD/BIM sources, project management, and your ERP — via secure connectors or MCP. The AI works within your revision and access controls, so it surfaces the current issue and flags superseded ones rather than serving an out-of-date detail; for older systems without clean APIs we build structured wrappers so you avoid a risky migration. What's realistic depends on your formats and storage, which we assess honestly in discovery.

Yes — this is one of the highest-ROI deployments for engineering firms. The system reads an ITT or brief, extracts the requirements, retrieves your most relevant won projects, methodologies, project references, and CVs, and drafts a structured first response against your template — flagging gaps where information is missing. Your bid team refines and approves; the AI never submits anything. Typical saving is a large share of the senior time a tender consumes, which lets you pursue more opportunities and submit better-tailored, more competitive bids.

Yes — compliance and consistency checking is a strong, lower-liability use case because the AI surfaces issues for an engineer to confirm rather than making the final call. It can check drawings, specs, and submissions against codes, client requirements, and your own standards, flagging likely non-conformances, missing items, and inconsistencies between documents far faster than manual review. The engineer remains the decision-maker; the AI just makes sure fewer things slip through. On fixed-fee work, catching issues before issue directly reduces rework and risk.

Yes to both. We support deployments from your managed cloud tenant (AWS, Azure, GCP) to fully self-hosted environments running open-weight models where data never leaves your perimeter — so for strict client-confidentiality, government, or defence-adjacent work, sensitive project data is processed inside your boundary and we never need direct access to it. And ownership is unconditional: your project data, the system we build, the outputs, and all code are yours. We sign NDAs before any discovery conversation, never retain your data after an engagement, and never use it to train models that benefit other clients — and we transfer knowledge deliberately (documentation, runbooks, pairing) so your team can own and operate the system rather than being locked into us.

It's a question worth raising with your insurer early, and a documented, engineer-in-the-loop process is exactly what reassures them. Because a qualified engineer reviews and owns every liability-bearing output — with citations and an audit trail showing what was AI-drafted versus engineer-checked — you can demonstrate that AI is an assistive tool inside your existing QA, not an unchecked decision-maker. Increasingly, clients and insurers ask how firms use AI on technical work; being able to show a controlled, auditable process is becoming a trust advantage rather than a liability.

Not perfectly — but document and data maturity is the single most underestimated factor, so we assess it honestly in discovery. The trick is to choose a first use case whose data is already reasonably accessible in your EDMS, and to scope around the systems you actually have. Where a lot sits in scanned paper or scattered drives, we either build the access and preparation layer as part of the work or steer you to a different first use case. A readiness gap is a reason to scope carefully, not a reason to wait.

Yes. Production AI needs ongoing attention, and we offer managed support and MLOps. Our standard engagement includes a period of included support after launch — monitoring, incident response, performance review, and minor adjustments. Beyond that, retainers cover model-drift monitoring, re-evaluation as codes and standards are updated, cost optimization, feature additions, and integration maintenance as your project systems change. Because models degrade quietly rather than failing loudly, this monitoring is what keeps quality and trust intact long after launch — and it's why we define operational ownership before go-live.