EngineeringPals® Consulting

Data Science & Automation

With years of experience in data science and automation for engineering teams, we can develop bespoke dashboards, automation tools, 3D visualisation interfaces, perform complex calculations, and design and implement optimisation solutions.

Typical projects include interfacing meta-heuristic algorithms without engineering CAD/CAM software, training and validating CNNs, MLPs, and RNNs in TensorFlow and PyTorch, automating reporting with Python and LaTeX, building web apps with Flask and PostgreSQL, building live dashboards with Plotly, Dash, and automating data pipelines with Airflow.

Typical engagement 1–3 weeks (scope dependent) Deliverables Weekly updates and detailed final report. Confidentiality NDA-friendly. Secure file handling. Commercial Projects from £3k (scope dependent)

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Engineering dashboard and automated reporting

How We Work

Our projects are delivered thoroughly and systematically. EngineeringPals' typical delivery process is provided below:

Intro call (free, 30 min): Discuss goals, time constraints, available data/materials, and IP/NDA agreements.
Scope confirmation email: Within 3 business days, you receive a 1–2 page Scope Confirmation document, detailing deliverables, inputs required, assumptions/exclusions, timeline, price, and change control.
Statement of work (SoW): Once you approve the Scope Confirmation, we issue the Statement of Work for signature and confirm the start date. IP and NDA agreements are also finalised at this stage.
Kickoff (60 min): Technical handover to share files, confirm methods/standards for implementation, and finalise delivery plan details not contained within the SoW.
Delivery (phased): Delivered in short phases with a weekly written update: completed, next steps, questions, and risks.
Review pack: Draft review pack: inputs log, assumptions register, analysis/test method, results summary, verification/correlation evidence (where applicable), and recommended next actions.
Final handover: Final report + deliverables bundle (analysis files, scripts, CAD, datasets—per scope), with release notes and a handover call to confirm how to reproduce/extend the work.

FAQs

What software do you use for data science and ML?

Do you use deep learning frameworks like TensorFlow or PyTorch?

Do you build “ChatGPT-style” embedding search for engineering content?

Can you integrate ML or optimisation loops with FEA workflows?

Can you build internal tools with web-dev + SQL in the stack we already use?

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