8x More Bids Submitted | AI Tender Automation

Client Bio

Intrepida is a US-based, veteran-owned consulting firm working in finance, accounting and software development. A meaningful share of its growth depends on public tenders - and on finding the right ones and responding before they close.

That work was entirely manual. Someone had to search for opportunities, read each one to judge whether it was worth pursuing, gather the relevant material, and write the proposal. Fourteen to twenty-two bids a month was what that process could carry.

As the client puts it: "Our tendering was entirely manual, and the time it took decided how much we could go after."

Intrepida asked us for one thing, clearly stated: automate it. Find the matching tenders, prepare the response, produce a proposal ready to submit.

Metric	Before (manual process)	After (agentic AI workflow)	Δ
Manual effort in the bidding process	Fully manual, end to end	Under a tenth of the work	Over 90% automated
Bids submitted per month	14–22	Min 112	8x
Time from tender published to proposal ready	Days	Hours	Client's words: "a fraction of the time"

Metric

Before (manual process)

After (agentic AI workflow)

Manual effort in the bidding process

Fully manual, end to end

Under a tenth of the work

Over 90% automated

Bids submitted per month

14–22

Min 112

Time from tender published to proposal ready

Days

Hours

Client's words: "a fraction of the time"

Challenges

Time decided volume
Fourteen to twenty-two bids a month was the ceiling the manual process could carry, regardless of how many suitable tenders existed.

Opportunities lost to the clock
Tenders that were a genuine fit went unanswered because there was no time left to prepare a serious response.

Discovery buried in reading
Matching opportunities had to be found by working through listings by hand, one judgement at a time.

Proposals rebuilt from scratch
Every response was written anew rather than assembled, even where much of the substance repeated.

Judgement held in people, not in the process
What made a tender worth chasing, and what a winning response contained, lived in the heads of the people doing the work.

Our Solution

We built end-to-end automation of the tendering process using an agentic AI approach, with context engineering doing the heavy lifting: the system does not merely generate text, it works from a structured picture of what Intrepida does, what it has bid on before, and what a qualifying opportunity looks like for this firm specifically.

"They learned how we actually bid before they built anything - the process reflects our judgement, not a template."

The workflow covers:

Automated tender discovery: continuous monitoring of public tender sources, so matching opportunities surface on the day they publish rather than whenever someone reaches the listings.
Automated fit assessment: each opportunity evaluated against the firm's capabilities and bidding history before it reaches a person.
Proposal preparation: draft responses assembled from the client's own material and prior submissions, structured to the requirements of the specific tender.
A human decision point where it matters: over 90% of the work is automated; the decision to submit is not. As the client puts it, "the submission decision stayed with a person, which mattered to us."
Notification in the tools already in use: opportunities and drafts surface in the team's existing workspace instead of another system to check.

Delivered in three months, co-developed with the client. The workflow was designed only after we had worked through the firm's complete bidding process with them - how they judge a tender, what a strong response looks like, where a human decision is genuinely required. The automation encodes their judgement rather than a generic one, which is why it produces proposals they are willing to submit under their own name.

Technologies: Anthropic Claude · n8n · Slack

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