What Capture Management Looked Like Before AI — And What It Looks Like Now
It's Tuesday morning. A 15-person IT services firm in northern Virginia just got an alert: the Department of Veterans Affairs posted an IT support services contract on SAM.gov. Small business set-aside. Best value evaluation. $4.2 million ceiling over five years. Response due in 21 days. The firm has relevant past performance, the right NAICS code, and a team that can do the work. On paper, this is exactly the kind of opportunity they've been looking for. The question is: do they actually understand what this opportunity requires, who they're competing against, and whether it's worth pursuing — before they commit 60+ hours to a proposal? This is the same pursuit, told two ways.
TL;DR
- Capture is the real bottleneck. By the time most small firms start writing a proposal, the outcome is already shaped by how well they discovered the opportunity, understood the solicitation, assessed the competition, and made the bid decision. That's capture — and it's where the biggest time and intelligence gaps live.
- The manual capture workflow still dominates most small and mid-sized firms: portal searching, document downloading, manual reading, spreadsheet-based tracking, and go/no-go decisions driven by instinct rather than data. It costs 20–30 hours per opportunity before a single word of the proposal gets written.
- Generic AI (ChatGPT) can't read full solicitation packages, has no company model, hallucinates compliance details, and can't track procurement forecasts or pre-solicitation signals — the early lifecycle intelligence that determines whether you're starting capture on day one or day minus ninety.
- Purpose-built GovCon AI restructures capture around three inputs: a company model, the full solicitation record, and live market data. Discovery, analysis, and bid decisions happen in a single connected system grounded in your data and the actual documents.
- The shift isn't just speed — it's role transformation. Teams move from document analyst to deal strategist. The firms pulling ahead in 2026 aren't the ones reading faster — they're the ones who stopped reading manually altogether.
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The Opportunity
Before we walk through the two versions, here's the opportunity both versions are pursuing. This is a composite based on real solicitations — the kind of contract that small IT services firms see every week on SAM.gov.
IT Support Services — Department of Veterans Affairs
This is a real, winnable opportunity for the right firm. The question isn't capability — it's whether the capture process gives the team the intelligence they need to make a confident bid decision and enter the proposal phase with a clear strategy. Here's how that plays out.
Part One: Manual Capture
Most capture teams still lean on saved searches and inbox alerts. But as CLEATUS co-founder Yigit Guney wrote in GovCon Wire, the real signals live inside the PWS, SOW, Section J attachments, Q&A exchanges, and amendments — not in the synopsis or title. Surface-level monitoring tells you something was posted. It doesn't tell you whether it fits, what the documents actually require, or how the competitive landscape looks.
Here's what that means in practice.
Finding the Opportunity — On Posting Day
Sarah runs business development for the firm. Her morning starts the way it has for the past three years: she opens SAM.gov, logs in through Login.gov (which requires re-authentication because it's been more than 30 days), and starts searching.
She searches by NAICS code. Scrolls past dozens of results that don't fit — wrong agency, wrong scope, full-and-open competitions her firm can't win, IDIQs they don't hold. She bookmarks a few possibilities and keeps scrolling. She checks two state procurement portals for her region. Nothing new today.
An hour and a half in, she finds the VA opportunity. The title is vague — "IT Support Services" — but the description mentions help desk, network monitoring, and endpoint management, which aligns with their capabilities. She clicks through to the solicitation.
What she doesn't know — and can't easily discover through SAM.gov's interface — is that this opportunity had been visible for months before it was formally solicited. The VA posted a Sources Sought notice six months ago, asking industry whether small businesses could perform this scope. Three months later, a pre-solicitation notice appeared on SAM.gov signaling the RFP was coming. And the VA's own procurement forecast on the Forecast of Contracting Opportunities (FCO) tool had listed this requirement even earlier — with the estimated award date, NAICS code, and set-aside designation.
Sarah didn't see any of that. She wasn't monitoring the FCO tool — most small contractors don't even know it exists. She wasn't tracking Sources Sought notices because they aren't solicitations and don't show up in her saved searches filtered by response deadline. By the time she finds the actual RFP, the firms that tracked the forecast and responded to the Sources Sought have already shaped their teaming arrangements, engaged the contracting officer with clarification questions during the pre-solicitation phase, and started their capture planning weeks ago.
She's finding the opportunity on posting day. Her best-positioned competitors found it months ago.
Time spent on discovery this morning: 2 hours. And this is just today. She does this every morning, five days a week. That's roughly 10 hours per week spent on portal searches alone — a pattern confirmed across the industry. The 2025 Deltek Clarity GovCon Industry Study found that finding opportunities too late was the top business development challenge reported by contractors, and that top-performing firms are those using AI, automation, and early lead identification to stay ahead.
Reading the Solicitation — Three Days of Document Archaeology
Sarah downloads everything. The base solicitation is a 112-page PDF. There are two amendments — one changes the questions deadline, the other modifies a CLIN description. The Performance Work Statement is a separate 28-page Word document. There's an Excel pricing template. A Quality Assurance Surveillance Plan. A past performance questionnaire that needs to be sent to three references.
She starts reading. Section A tells her the basics — solicitation number, contracting office, dates. Section B lays out the CLINs — base year plus four option years, firm-fixed-price, with separate line items for help desk support and on-site technicians. She takes notes in a Word document.
Section C references the PWS — but the PWS is in a separate attachment listed in Section J. She opens the Word doc and starts marking up requirements. "The contractor shall provide Tier 1 and Tier 2 help desk support 24/7/365." "The contractor shall maintain a minimum 95% first-call resolution rate." "All personnel shall possess a minimum of CompTIA Security+ certification."
She gets to Section H — Special Contract Requirements — and finds a paragraph requiring all contractor personnel to undergo a VA background investigation, with suitability determinations completed within 30 days of contract start. That has staffing implications. She makes a note.
Section L tells her what to submit: a technical volume (15-page limit), a management volume (10-page limit), a past performance volume (no page limit but three references required), and a separate price volume using the provided template. Section M says technical is "significantly more important" than price, and management is "approximately equal" to price. Past performance is evaluated on a confidence scale.
By the end of day three, she's read most of the documents. She's filled six pages of notes. She still hasn't cross-referenced the two amendments against the base solicitation to confirm what changed. She hasn't checked whether the attachments listed in Section J are all included in the download. She hasn't verified the past performance questionnaire requirements.
Time spent on solicitation analysis: 10–12 hours across three days.
The Go/No-Go — A Decision Built on Instinct
Sarah brings the opportunity to her firm's leadership for a go/no-go decision. She's prepared a one-page summary, but the conversation still takes 45 minutes because the leadership team has questions she can't fully answer from memory: What's the incumbent's contract history? How many small businesses bid last time? What's the realistic price range?
She goes back to her desk and starts the competitive research. Since FPDS was decommissioned in February 2026, she logs into SAM.gov's contract data search — which now requires a separate navigation path from the opportunity search she did earlier. She searches for the incumbent by solicitation number. She finds the previous award but can't easily see the full award history or pricing trends without exporting data to a spreadsheet and manually pivoting it.
She Googles the incumbent contractor. She checks their SAM.gov registration to see their size, NAICS codes, and certifications. She checks LinkedIn to see if they're hiring for VA-related positions (a signal they expect to win the re-compete). She logs into USASpending.gov to cross-reference award amounts.
This patchwork of research takes another half-day. And at the end of it, she has data but not intelligence. She knows who won last time and roughly what they charged. She doesn't know their performance rating (CPARS is still migrating to SAM.gov). She doesn't know how many firms are likely to bid. She doesn't have a probability-of-win assessment based on anything more rigorous than instinct.
The team decides to bid. It's Thursday of the first week. They have 16 days left, and the real work — building a compliance matrix, writing the proposal, assembling the cost model — hasn't started. The capture phase consumed nearly half their available calendar time and gave them incomplete intelligence to show for it.
Time spent on go/no-go and competitive research: 6–8 hours.
Manual Capture: Total Accounting
| Capture Phase | Activities | Hours |
|---|---|---|
| Discovery | Portal searching, filtering, initial review — no pre-solicitation tracking | 2 hours (today) + ~10/week ongoing |
| Solicitation analysis | Reading all documents, note-taking — amendments not fully reconciled | 10–12 hours |
| Competitive intel & go/no-go | Incumbent research across 4+ portals, leadership briefing | 6–8 hours |
| Total capture time | Before a single word of the proposal is written | 18–22 hours + ongoing search overhead |
And that's for a firm that knows what it's doing. Less experienced teams spend even more time — or worse, spend the same time and still enter the proposal phase with critical gaps in their understanding of the requirements, the competition, and the evaluation criteria.
The real damage isn't the hours. It's that the capture phase produced partial intelligence, consumed five calendar days, and left the team entering the proposal phase behind schedule with incomplete understanding. Sarah still hasn't fully reconciled the amendments. She missed a QASP requirement she won't discover until the compliance review. She has no idea how many firms are bidding. And her competitors who tracked the forecast and Sources Sought have been running capture for months.
The capture phase is supposed to answer three questions: Is this a fit? Can we win? How should we position? Manual capture gives Sarah approximate answers to the first, guesswork on the second, and no structured foundation for the third.
Part Two: AI-Powered Capture
Same firm. Same opportunity. Same team. Different architecture.
The distinction matters. This isn't about bolting a chatbot onto the existing workflow. As Yigit wrote in GovCon Wire, the shift is from metadata to meaning — from chasing notices and alerts to making decisions grounded in what the documents actually say. Purpose-built GovCon AI combines three inputs: a company model, the full solicitation record, and live market updates. The result is fit-first triage, not keyword matches.
The Opportunity Was Already Waiting — Because Capture Started Months Ago
Sarah doesn't search SAM.gov on Tuesday morning. She hasn't manually searched a procurement portal in months.
But this pursuit didn't start today. It started months ago — when the VA first signaled its intent.
CLEATUS's Auto Capture monitors more than active solicitations. It tracks the full pre-solicitation lifecycle: agency procurement forecasts published through the government's Forecast of Contracting Opportunities (FCO) tool, Sources Sought notices, Requests for Information, and pre-solicitation notices on SAM.gov. These early signals — which most small contractors either miss entirely or don't have time to track across dozens of agency forecast pages and portal filters — are where the highest-value capture intelligence lives.
Six months ago, CLEATUS flagged the VA's Sources Sought notice for this requirement and scored it against the firm's company model: their capabilities, NAICS codes, past performance history, set-aside certifications, geographic footprint, and contract-size sweet spot. Sarah saw it in her pipeline, noted the fit, and filed it as a future pursuit. Three months ago, when the pre-solicitation notice appeared, CLEATUS updated the opportunity record automatically and bumped it up in priority. Sarah used that window to identify a potential subcontractor with complementary VA experience and started a preliminary staffing plan.
Now, on posting day, the full solicitation lands in her pipeline already connected to that history. She's not starting from zero. She's been tracking this opportunity through its entire lifecycle — from forecast to Sources Sought to pre-solicitation to live RFP — without manually checking a single portal.
The match score explains itself. Three relevant past performance references in the IT support domain. Two with VA specifically. NAICS and set-aside alignment confirmed. Geographic proximity to the VA facility. Contract size within their sweet spot. The reasons for the ranking are visible — not a black-box score but an explanation the team can interrogate.
Time spent on discovery: 15 minutes of reviewing a curated pipeline. No weekly search overhead — the monitoring is continuous across federal, state, and local sources, all normalized into one view. And the pre-solicitation tracking gave Sarah a months-long head start on teaming and strategy that simply isn't possible when you first learn about an opportunity on posting day.
Document-Grounded Intelligence in Minutes, Not Days
Sarah clicks into the opportunity. The Contract Breakdown is already complete.
This is what Yigit calls "document-grounded intelligence" — treating the solicitation package as the source of truth. CLEATUS parsed every page of every document: the 112-page base solicitation, both amendments, the PWS attachment, the QASP, the pricing template, and the past performance questionnaire. It extracted obligations, deliverables, milestones, submission instructions, and evaluation factors into structured outlines the team can act on immediately — organized into the Uniform Contract Format (Sections A through M).
She can see immediately:
Section A: Solicitation number, contracting office, submission deadline (with the amendment-updated date already reflected — no manual cross-referencing needed).
Section B: Five CLINs — base year plus four options, FFP, with separate line items for help desk and on-site support. Option year pricing requires escalation.
Section C: Full scope extracted from the PWS, including the 24/7/365 help desk requirement, the 95% first-call resolution SLA, and the CompTIA Security+ certification mandate for all personnel.
Section F: Performance period dates, including the 30-day transition-in requirement.
Section H: VA background investigation requirement flagged as a special condition with staffing timeline implications.
Section L/M: Proposal structure mapped out — 15-page technical limit, 10-page management limit, three past performance references required. Evaluation weighting clearly summarized: technical significantly more important than price, management approximately equal to price, past performance evaluated on a confidence scale.
Sarah didn't paste anything. She didn't write a prompt. She didn't manage token limits. She didn't wonder whether she missed a requirement in an attachment she hadn't opened yet.
She does, however, catch something the AI structured but didn't flag as unusual. A paragraph from Section H that the Contract Breakdown categorized as a standard data rights clause is actually more restrictive than typical — it requires the contractor to waive rights to any tools or methodologies developed during performance. That's a significant business risk that changes pricing and technical approach. She flags it manually and adds a note. The AI structured the information; her domain judgment caught the nuance.
When she has questions — "Does this solicitation require CMMC compliance?" or "What are the specific SLA penalties?" — she asks the GovCon Copilot in plain language and gets cited answers pointing to the exact clause, page, and attachment. No hallucinated responses. No guesswork.
And the amendments? CLEATUS didn't just incorporate them — it summarized what changed and flagged the implications. Amendment 001 moved the questions deadline. Amendment 002 modified a CLIN description and added a deliverable. The platform shows the "what changed" and the "so what" together, so Sarah doesn't have to Ctrl+F her way through a redline to figure out whether the amendment affects her approach.
Time spent on solicitation analysis: 45 minutes of reviewing structured intelligence and asking targeted follow-up questions. Not 10–12 hours of raw document reading.
The difference isn't speed — it's completeness. In the manual version, Sarah spent 10+ hours reading and still hadn't fully reconciled the amendments by the time she moved to go/no-go. She'll later discover a missed QASP requirement during the compliance review — four days before submission. With the Contract Breakdown, every requirement across every document was extracted and organized before she started. The intelligence gaps that plague manual capture simply don't exist.
A Go/No-Go Built on Strategy, Not Instinct
Sarah opens the opportunity assessment. CLEATUS has already assembled the competitive context:
Incumbent data: The previous contract was awarded to a specific firm at a specific price point. The contract has been running for four years — this is a full re-compete, not an option renewal. CLEATUS shows the incumbent's award history with the VA, including two other active contracts in the same NAICS code — a signal that the incumbent has deep agency relationships and will likely compete aggressively.
Competitive landscape: CLEATUS identified three other small businesses that have won similar VA IT support contracts in the past two years, along with their typical pricing ranges and past performance profiles. One of them responded to the same Sources Sought notice six months ago. Another recently posted VA-related job listings — a potential signal they're staffing up for a bid.
PWin indicators: The platform assessed alignment across multiple dimensions — NAICS match, set-aside eligibility, past performance relevance (three contracts in the same domain, two with VA specifically), geographic proximity, and team qualification gaps. The overall score is strong. But it flagged two risk factors: the incumbent's four years of institutional knowledge, and the fact that the solicitation's transition-in timeline is aggressive — only 30 days — which typically favors the incumbent or a firm with pre-existing VA-cleared personnel. Sarah's firm has two employees with active VA suitability determinations from a previous contract, which partially mitigates the risk.
Pre-solicitation history: Because CLEATUS tracked the opportunity from the forecast stage, it also shows that four firms downloaded the Sources Sought documents — giving Sarah a rough sense of competitive density before the RFP was even posted.
Sarah brings this to the go/no-go meeting. Instead of a 45-minute conversation driven by incomplete data, the discussion focuses on strategy: whether to formalize the teaming arrangement she identified during the pre-solicitation phase, how to position against the incumbent's institutional knowledge advantage, and which win themes will resonate with VA evaluators. The decision to bid takes 15 minutes because the data already answered the threshold questions.
Time spent on go/no-go and competitive research: 1 hour total — including the meeting.
AI-Powered Capture: Total Accounting
| Capture Phase | Activities | Hours |
|---|---|---|
| Discovery | Review curated pipeline (tracked since forecast stage) | 15 minutes |
| Solicitation analysis | Review Contract Breakdown, Q&A with citations, amendment tracking | 45 minutes |
| Competitive intel & go/no-go | Review PWin data, competitive context, strategy-focused leadership call | 1 hour |
| Total capture time | From pipeline review to bid decision | 2 hours |
Same opportunity. Same team. But instead of 18–22 hours of capture work spread across five calendar days — with incomplete intelligence to show for it — the team completed capture in 2 hours on day one. They have a structured understanding of the requirements, a data-backed competitive assessment, a clear bid strategy, and 20 calendar days to execute the proposal with the full context of the solicitation already organized.
And because CLEATUS tracked the opportunity from the forecast stage, Sarah had months of pre-solicitation capture time that the manual version didn't even know was available. She identified a teaming partner. She pre-screened personnel for VA suitability. She entered the RFP phase with a strategy, not a scramble.
The capture phase answered all three questions: This is a strong fit. We can compete — with specific mitigations for the incumbent advantage. And here's how we should position: lead with VA experience, highlight pre-cleared personnel, team for complementary depth, and price against the incumbent's likely rate structure.
From Document Analyst to Deal Strategist
The numbers tell one story. The role transformation tells a more important one.
Yigit framed this as the move "from document analyst to deal strategist." With first-pass reading and extraction automated, teams invest their hours in stakeholder conversations, teaming strategy, requirement shaping, and win theme development — the activities that actually differentiate one bid from another. The goal is 80% of team time spent using information, not finding it.
That shift shows up in three specific ways.
Earlier Engagement, Not Just Faster Response
In the manual version, Sarah found the opportunity on posting day. In the AI-powered version, she'd been tracking it for months. That difference isn't about speed — it's about when capture begins.
The firms with the highest win rates in government contracting don't start capture when the RFP drops. They start during the forecast and Sources Sought phases — engaging contracting officers, attending industry days, submitting capability statements, and building teaming arrangements before the clock starts. That's the discipline that separates firms with 40%+ win rates from the rest.
But that discipline requires awareness of early signals across hundreds of agency forecast pages, SAM.gov notice types, and procurement planning documents. Manually monitoring all of that is a full-time job. CLEATUS makes it automatic — surfacing pre-solicitation signals scored against your company model so you can engage early on the opportunities that actually fit.
Sharper Bid/No-Bid Decisions
In the manual version, the go/no-go decision was made with incomplete data and instinct. In the AI-powered version, it was made with competitive context, scoring data, pre-solicitation history, and past performance alignment. That difference compounds over time.
Early, objective briefs on requirements and evaluation factors reduce false starts and late pivots. Leadership gets a clearer picture of pipeline quality — not just volume. Firms that make data-informed bid decisions pursue fewer bad-fit opportunities and more good-fit ones. Over time, that means higher win rates, better past performance ratings, and a reputation with agencies that opens doors. It's a virtuous cycle — but it only works if capture generates enough intelligence to make the decisions well, which the manual process rarely does.
Better Pursuit Strategy and Collaboration
When writers, pricers, and subject matter experts all work from the same structured requirements and evaluation criteria — extracted from the actual documents, not paraphrased from someone's reading notes — the downstream proposal work starts from a stronger foundation. Win themes stay consistent from capture through submission. Compliance gaps surface before the writing starts, not during the final review. And the team enters the proposal phase with time and intelligence on their side, not deadline pressure and guesswork.
– John Garnish, Business Development Lead, D2 Government Solutions
Why Generic AI Doesn't Close the Gap
Some firms have tried to split the difference — keeping their manual workflow but adding ChatGPT to speed up specific tasks. In practice, this creates a third version that has the limitations of both approaches.
ChatGPT can't read full solicitation packages, cross-reference amendments against a base RFP, or verify that every Section J attachment is present. It has no company model — no encoded understanding of your certifications, past performance, or contract history. And it can confidently produce outputs that hallucinate clauses or miss requirements modified by an amendment you haven't pasted in yet. The output looks professional. The risk is hidden.
More critically for capture: ChatGPT can't track procurement forecasts, Sources Sought notices, or pre-solicitation signals. It has no awareness of the competitive landscape — who won the last contract, what they charged, or how many firms are likely to bid. The entire front end of the capture lifecycle — the intelligence that determines whether you're making a data-informed bid decision or a gut-instinct gamble — is invisible to it.
The workflow also fragments further: SAM.gov for discovery, ChatGPT for extraction, Word for notes, Excel for tracking, USASpending for award data. You're the integration layer across five disconnected tools. Context is lost between every tab switch.
This isn't an argument against ChatGPT — it's useful for many tasks. But for capture management, where cross-document reasoning, competitive intelligence, and lifecycle tracking are non-negotiable, generic AI creates a false sense of productivity while leaving the structural problems unsolved.
What the Numbers Say
The capture transformation isn't theoretical. Here's what contractors are reporting:
D2 Government Solutions, an SDVOSB with 300+ employees, was spending 8 hours daily searching for opportunities across portals. With CLEATUS, they achieved 75% faster opportunity discovery — and the capture intelligence foundation helped them triple proposal output with the same team.
Operation Hired replaced their "cluttered" mix of generic AI tools and spreadsheets with CLEATUS. The GovCon Copilot became their "first-read" expert — breaking down solicitations instantly so the team could make faster, more confident bid decisions. Result: 6x proposal throughput in 10 weeks.
MST Maritime went from spending most of their week hunting opportunities and parsing solicitations to pursuing 10+ opportunities per month. 75% faster opportunity discovery and 4x faster solicitation comprehension — with the same lean team.
Ron's Cycle Shop, a veteran-owned small business, cut opportunity search time from 40 hours per week to 2 and achieved a 90% win rate on targeted bids. For a firm of one, the capture transformation was the difference between spinning wheels and winning contracts.
LIS Solutions scaled SLED capture with 75% faster opportunity discovery and 4x faster solicitation comprehension, navigating the fragmented state and local procurement landscape from a single workspace.
These aren't cherry-picked results from firms with unlimited resources. They're outcomes from small and mid-sized contractors who made one change: they stopped treating capture as a manual process.
The Competitive Baseline Has Moved
The 2025 Deltek Clarity Study found that AI adoption among government contractors surged to 45% in 2025, up 10 percentage points in a single year. The top-performing firms — those with the highest win rates — are disproportionately the ones using AI for early lead identification and capture automation. Finding opportunities too late was the top business development challenge across the entire survey.
As Yigit wrote: "AI is now setting the competitive baseline. Systems that learn your company, read the full record, track changes with context, normalize sources into one view and rank by fit are moving from nice-to-have to necessary."
The manual capture process was the only option when it was the only option. It's not anymore. And every month you stay on the manual path, the firms that switched are pulling further ahead — not because they have bigger teams, but because they've shifted from finding information to using it.
What happens after capture: This post focuses on the capture phase — discovery, analysis, and bid decisions. But the quality of capture directly shapes everything downstream. When the solicitation is fully structured and competitive intelligence is assembled, the proposal team starts from a position of strength. CLEATUS's AI Proposal Suite builds directly on the capture foundation — generating compliance matrices, Section L/M-aligned outlines, and drafts grounded in the actual solicitation and your past performance. For the proposal side of the story, see Stop Prompt Engineering. Start Winning Contracts. and our guide to 10 AI Prompts to Decode Any Government Solicitation.
Ready to see the difference? Start your free trial or book a live demo and walk through the AI-powered capture workflow with your current pipeline.
Want the full framework? Read Yigit Guney's GovCon Wire piece on context-aware AI for modern capture — it lays out exactly why the shift from alerts to insight is the new competitive baseline.
Frequently Asked Questions
Further Reading
- From Alerts to Insight: Context-Aware AI for Modern Capture (GovCon Wire)
- Stop Prompt Engineering. Start Winning Contracts.
- 10 AI Prompts to Decode Any Government Solicitation in 30 Minutes
- FPDS Is Gone. Here's How Smart Contractors Are Accessing Government Contract Data Now.
- NAICS 511210: The #1 IT NAICS Code for Federal Contracts in 2026
- DIBBS Explained: How to Find and Win DLA Contracts
Customer Stories
- How D2 Government Solutions Tripled Growth Without Adding Staff
- How Operation Hired Achieved 6× Proposal Output with CLEATUS AI
- How MST Maritime Quadrupled Proposal Output with CLEATUS AI
- How a Veteran-Owned Shop Won Their First Contract with CLEATUS AI
- How LIS Solutions Cut SLED Capture Time by 75% with CLEATUS AI
About CLEATUS
CLEATUS is an AI-powered government contracting platform that helps contractors find opportunities, analyze requirements, track competitors, and win more contracts — at a fraction of traditional capture costs. We aggregate federal, state, local, and city opportunities; our GovCon Copilot analyzes solicitations and your internal documents to deliver actionable market intelligence that drives revenue growth.