The 6-Week Sourcing Booster Programme for Renewable Energy.

AI on one tender does not compound.

You may already use AI to draft RFx content, interrogate old tenders or compare documents. That is useful. It does not, by itself, build a sourcing capability that gets better with each event.

A custom GPT or internal agent can analyse historical documents. The harder question is what happens next:

• which requirements become reusable across the next wind, solar or BESS project
• which evaluation criteria should carry forward
• which clarification exposed a weakness in the original RFx
• which governance rule mattered when the lender’s technical adviser asked
• which project-specific deviation must not quietly become the new standard

And how any of it reaches the next live sourcing process, rather than disappearing back into documents, prompts and people’s memories.

The Booster turns sourcing history into a living sourcing operating system, and lets every event improve it.

Start with the sourcing history you already have.

Before the first live tender, give us the material your team already holds from a few representative events. No new documentation exercise. No remodelling anything for DeepStream.

You bring RFx packs, supplier responses, commercial schedules, clarification exchanges, evaluation files, approvals and award records.

DeepStream AI identifies the requirements and questions worth reusing, the scoring structures that worked, recurring clarification themes, governance and approval logic, and where standard content has drifted into project-specific exceptions.

Procurement decides what becomes reusable.

That becomes the first version of your sourcing operating system, before a single tender goes out.

Then run one real tender.

Your first live event does not begin from a blank page. It begins from what your organisation has already done.

Requirements → AI-assisted RFx → Governance → Supplier response → Comparison → Clarification → Evaluation → Award

The point is not that AI makes that tender faster. The point is what it teaches you: better questions, supplier interpretations you had not anticipated, clarifications that should have been designed out earlier, commercial structures that held up against a constrained supply base.

All of it becomes the starting point for the next one.

Six weeks. One real tender. A much bigger asset at the end.

The Booster is not designed to prove DeepStream can run one tender. That would be a small ambition.

It is designed to show whether you can build something more valuable: a sourcing operating system that learns from work you are already doing.

By the end of six weeks you should know:

• how quickly existing sourcing knowledge can be reused on the next project
• where manual RFx preparation disappears
• how much easier supplier responses are to compare
• what the first tender added to your reusable knowledge
• how much stronger the next one starts as a result

What worked gets reused. What changed stays explicit. What suppliers taught you is not lost.

Why not build this with your own AI stack?

You should use your own AI where it creates advantage. Claude, enterprise LLMs, agents, retrieval, your own orchestration.

The question underneath is different: where do you create advantage by building, and where are you rebuilding sourcing infrastructure?

Live sourcing carries thousands of decisions that are easy to underestimate until you manage them repeatedly. Bid versions. Supplier boundaries. Permissions. Deadlines. Evaluation states. BAFOs. Approval routes. Award controls. Audit history.

A model can reason over procurement information. An execution layer has to establish what is current, what is true, who can see what, who can change what, what needs approval, and what enters the formal record.

That is not prompt engineering. It is sourcing infrastructure.