Applied AI and software delivery for real operations.
We work alongside energy and renewables teams to build AI tools around their workflows. We agree the data access, review steps and measures of success before building.
Document-heavy work with real cost
- Engineering outputs that take too long to assemble
- Knowledge buried across PDFs, emails, SharePoint, and archives
- Assurance work that depends on traceability and auditability
We agree how to measure the workflow before building.
Embedded AI engineering
Work alongside an engineer who can understand your process and build a tool around it.
We start with one recurring workflow, the people doing it and the systems they use. Together, we agree what the tool should do, which information it can access and where a person needs to review or approve its work.
AI can help read source material or prepare a draft within that workflow. We test the output with your team and agree how to measure its usefulness.
This way of working is often called forward-deployed engineering. On-site time, approved access, delivery scope and ongoing support are agreed for each engagement.
What we build and assess
We start with a clearly bounded workflow, the source it depends on and an agreed way to assess it.
Corporate knowledge retrieval
Build AI agents that find the right technical information fast, with references back to source documents.
Good for procedures, manuals, specs, and management system documents.
Workflow automation
Reduce repetitive drafting and assembly work. Keep outputs consistent and auditable.
Good for packs, reports, and document sets that repeat.
Custom AI tools
Build a custom tool where generic software and general chat tools do not fit.
For regulated workflows with defined access and review controls.
Fast, staged delivery
A staged process, so you get something usable quickly without locking into the wrong architecture.
Discovery
- Define the first workflow; you bring its source documents
- Confirm data sources and constraints together
- You identify who on your team will review the output
- Agree with that reviewer what an accepted result looks like and how to measure it
- Agree the security approach and deployment options
Pilot
- Build a pilot for the agreed workflow
- Test with real users and real documents
- Compare preparation, review and rework time, and record output quality and exceptions
Deploy
- Harden and integrate into your workflow
- Set access controls and audit expectations
- Train users and define ongoing support
Proof before expansion
We prove value with a single workflow, then expand from there.
We start with constraints
Deployment arrangements depend on the product and engagement. We agree hosting and data-residency requirements before building, including whether a private deployment is feasible. Limited data retention and auditability requirements are discussed at the same stage.
A focused accountability review
Synapse extracts documented responsibilities, links them to roles where the documents support the link, and keeps the source text for review.
You bring the management-system documents for one organisational event and agree who on your team will review the findings. That reviewer confirms, questions or defers each responsibility against its source text. Your team decides what is accepted; the document owner approves any proposed change of owner.
Frequently asked questions
What services does Clairvynt offer?
Three kinds of work for energy and renewables teams: knowledge retrieval with references back to source documents, workflow automation for packs and reports that repeat, and custom AI tools where generic software does not fit.
When does a custom AI tool make sense?
When a specific workflow needs it, for example a regulated process with defined access and review controls, or answers that must reference the source documents. We start by agreeing the workflow, the source it depends on and how to assess it.
Can we start with a small project?
Yes. We start with a single, clearly bounded workflow, prove its value, then expand from there.
How does a project start?
With discovery. We define the first workflow and how success is measured, confirm the data sources and constraints, and agree the security approach. You bring the source documents for that workflow and identify who on your team will review the output. Together, we agree what that reviewer needs to accept before building. A pilot with real users and real documents comes next, then deployment into your workflow.
How do you measure whether it works?
We agree how to measure the workflow before building. The pilot then compares preparation, review and rework time, and records output quality and exceptions. Your nominated reviewer uses those measures to assess the output against what was agreed in discovery.
Can you work within our security requirements?
Deployment arrangements depend on the product and engagement. We agree hosting and data-residency requirements before building, including whether a private deployment is feasible. Limited data retention and auditability requirements are discussed at the same stage.
Let's talk
We help energy and renewables teams produce structured, traceable output for document-heavy work. Share your use case and we'll outline if and how we can help.