# DB Civil Construction LLC: 14-Point Hit Rate Lift Model **By Dan Cumberland** · Published May 19, 2026 · Categories: AI Strategy > Hit rate is the ratio of proposals won to proposals submitted, and the SMPS Foundation's industry benchmark[^1] puts construction firms at 37.9%— the lowest of... ## What Hit Rate Means in Civil Construction \(and Why You Sit at the Bottom\) Hit rate is the ratio of proposals won to proposals submitted, and the SMPS Foundation's industry benchmark[1](/blog/blog-db-civil-construction-llc#ref-1) puts construction firms at 37\.9%— the lowest of the AEC disciplines, compared to 44\.2% for engineering[2](/blog/blog-db-civil-construction-llc#ref-2) and 42% across the industry overall[3](/blog/blog-db-civil-construction-llc#ref-3)\. That gap compounds across every pursuit cycle\. For context, a firm fitting this profile is DB Civil Construction, LLC— a DBE\-certified Florida civil contractor founded in 2017 in St\. Augustine, specializing in land clearing, underground utilities, roadway construction, and stormwater[4](/blog/blog-db-civil-construction-llc#ref-4)\. The LLC was filed with the Florida Division of Corporations on March 21, 2017 and remains active[5](/blog/blog-db-civil-construction-llc#ref-5)\. It represents the profile this analysis serves: owner\-led, public\-infrastructure\-focused, competing for work where one or two large pursuits a quarter can swing the year\. It is not a Dan Cumberland Labs client, and nothing here reflects their methodology or outcomes\. ```html-table
AEC DisciplineAverage Hit Rate
Engineering44.2%
Architecture (mid-range)~42%
Construction37.9%
AEC overall42%
``` Source: SMPS Foundation, *Measuring for Success* \(2016\)[1](/blog/blog-db-civil-construction-llc#ref-1)[3](/blog/blog-db-civil-construction-llc#ref-3) Ninety\-two percent of design and construction firms use hit rate as a primary BD KPI[6](/blog/blog-db-civil-construction-llc#ref-6)\. And firms compute it differently— some count verbal wins, some signed contracts, some invoiced work— which makes cross\-firm comparisons less clean than the dashboards suggest[7](/blog/blog-db-civil-construction-llc#ref-7)\. The bottom\-of\-AEC ranking is a selectivity problem, not a productivity one\. And the math gets uncomfortable fast\. ## The Selectivity Paradox: Why Saying No Raises Revenue Pursuing every available RFP doesn't grow revenue— it dilutes it\. AEC firms now win roughly half of the bids they submit[8](/blog/blog-db-civil-construction-llc#ref-8), meaning about half of the hours invested in proposals, meetings, and follow\-ups misses the mark entirely\. As the 2025 Unanet AEC Inspire Report puts it: "Half of all that time spent on proposals, meetings, and follow\-ups misses the mark\."[9](/blog/blog-db-civil-construction-llc#ref-9) The math of the bid pile is unforgiving\. Pursuit cost is largely fixed per bid— senior BD time, principal review, days of estimating, a captured\-team rehearsal\. A firm submitting fewer, better\-fit proposals reclaims capacity for capture work, win/loss debriefs, and client positioning that compounds the next quarter\. Chasing pennies when you could be chasing dollars— that's what every low\-fit pursuit is\. The work feels productive, but the dollars aren't where the bodies are\. Low\-fit pursuits are also typically low\-margin wins when they do close— the firm grinds harder for less\. **Three signs your firm is submitting too many proposals:** - The relationships behind your pursuits are thin— you're learning the owner's name from the RFP cover page\. - Capture work has disappeared from the calendar— no one is pre\-positioning for the bids you'd actually win\. - The principal is complaining about pursuit fatigue while still saying yes to every "interesting" lead\. One honest counter belongs here\. If your hit rate is 80% or higher, the issue may not be discipline— you may be underbidding and winning easy work at thin margins\. Hit rate is a signal, not a target\. If selectivity is the lever, the question is how much movement it can plausibly produce\. Here's the math\. ## The 14\-Point Hit\-Rate Lift: An Illustrative Model A civil construction firm operating at the SMPS construction baseline \(~38%\) could plausibly reach the broader AEC average \(~50%, per Unanet 2025\)[8](/blog/blog-db-civil-construction-llc#ref-8) by combining disciplined go/no\-go selectivity with AI\-assisted pursuit scoring— a swing of roughly 14 percentage points\. This is an illustrative model based on industry benchmarks, not a measured outcome attributed to any specific firm\. The 14\-point lift is the gap between where the average civil firm sits today \(SMPS baseline of 37\.9%\) and where the broader AEC market has already moved \(Unanet 2025's ~50% average win rate\)\. It's the closure of an existing industry gap, not a stretch goal\. ```html-table
MetricBaselineAfter Selectivity + ScoringChange
Bids submitted per year4028−30%
Avg pursuit hours per bid4040unchanged
Total pursuit hours1,6001,120−480 hrs
Hit rate38%52%+14 pts
Wins per year15.214.6≈ flat
Hours reclaimed for capture work—480 hrs/yrnet gain
``` This remains an illustrative model, not a client outcome\. Two co\-causes power the lift: selectivity and AI\-assisted pursuit scoring with pre\-positioning\. Neither does the work alone\. > What gets freed up isn't just hit rate\. It's the 30% reduction in pursuits that funds the capture work, the win/loss debriefs, and the principal time for relationship\-building that none of those wins came from anyway\. A few honest caveats\. The 50%\+ ceiling is realistic, not guaranteed\. Firms with clearer ICP get more benefit from scoring\. And pre\-positioning matters as much as selectivity— declining bids you wouldn't have won is the easy part\. The model only works if the firm can execute the selectivity\. That requires a scorecard the team will use— and a principal willing to follow it\. ## The Go/No\-Go Scorecard: What Belongs and Who Decides A go/no\-go scorecard for civil work weights four factors— Fit, Risk, Profitability, and Capacity[10](/blog/blog-db-civil-construction-llc#ref-10)— but only 40% of AEC firms currently use a formal one[11](/blog/blog-db-civil-construction-llc#ref-11)\. The other 60% rely on informal judgment, which is to say, on whoever is loudest in the room when the RFP lands\. The scorecard's job isn't to make the decision\. It's to surface the conversation a principal would otherwise avoid\. ```html-table
FactorSample CriteriaWeightScoring
FitRelationship strength with owner/GC; alignment with ICP; pre-positioning depth30%1–5
RiskBonding capacity match; geographic concentration; client payment history25%1–5
ProfitabilityRealistic margin vs. competitor floor; scope creep risk25%1–5
CapacityPM availability; field labor; superintendent bandwidth at start date20%1–5
``` Civil firms often weight Capacity higher than commercial GCs do— the work doesn't forgive a missing superintendent\. **Adoption mechanics that stick:** - **Standing committee\.** Principal, BD lead, ops lead review the pursuit list weekly\. Principal holds veto but does not run the meeting\. - **Cadence\.** Weekly review with a quarterly win/loss debrief feeding back into the scoring weights\. Without the loop, the scorecard ossifies\. - **A decision rule\.** A bid below threshold score \(say, 60%\) requires a written defense from whoever wants to pursue it\. The friction is the feature\. This is where [measuring AI success](/blog/measuring-ai-success) starts on the BD side— the quarterly debrief is the measurement system\. A scorecard makes the conversation possible\. AI accelerates it— but only if the firm has the data to feed it\. ## Where AI Helps the Decision \(and Where It Doesn't Yet\) AI\-powered pursuit tools accelerate the go/no\-go conversation by scoring opportunities against historical win patterns, flagging compliance and risk gaps, and standardizing the language a team uses to talk about pursuits\. They don't replace principal judgment\. And they fail badly when historical win/loss data is thin or inconsistent\. More than half of A&E firms now use AI, primarily in business development and IT[12](/blog/blog-db-civil-construction-llc#ref-12)\. Sixty\-seven percent of those firms expect AI to improve efficiency without reducing headcount[13](/blog/blog-db-civil-construction-llc#ref-13)\. Civil firms are leaning into AI for pursuit decisions, not for proposal\-writing replacement\. That distinction matters\. The right frame is intellectual augmentation— IA, not AI— [an AI strategy that augments principal judgment rather than replacing it](/services/ai-strategy/)\. **Current AEC tool landscape \(named factually, no endorsement\):** - **ContraVault**— Weighted go/no\-go scoring across Fit, Risk, Profitability, Capacity for Design\-Build/CMAR and public\-sector bids[10](/blog/blog-db-civil-construction-llc#ref-10)\. - **Joist AI**— AEC pursuit and qualification scoring\. - **Unanet ProposalAI**— Proposal automation paired with the Unanet aec360 CRM stack\. - **Inventive AI**— Proposal automation with go/no\-go workflows\. **Where AI helps now:** scoring opportunities against past wins; flagging bonding, compliance, and certification gaps before estimating starts; standardizing the team's pursuit language so reviews stop devolving into opinion\. **Where AI fails now:** without clean historical pursuit data, models produce confident wrong answers\. Mid\-market civil firms often don't have structured win/loss data yet— the first AI win is usually data structuring, not model running\. And proposal\-writing AI is a different category than pursuit\-decision AI\. Don't conflate them\. The scorecard and the tools both run aground on the same rock: getting the principal to actually say no\. ## The Cultural Blocker: Principals Hate Saying No The reason most civil firms don't adopt go/no\-go discipline isn't a missing tool\. It's a missing emotional permission for the principal to decline revenue\. Every "no" feels like leaving money on the table, even when the math says the opposite\. > The scorecard's most important job is protecting the principal from the principal's own instinct to chase everything\. The number is just the cover story for the conversation the team couldn't have without it\. The scorecard works as a cultural intervention because it externalizes the no\. "The scorecard said no" travels better through the team than "the principal said no\." That cover protects the relationships the firm depends on— the BD lead who wanted to chase it, the PM who was excited, the longtime client who suggested it\. Selectivity is downstream of strategic identity\. Firms with a clear ICP— who they serve best, where they win on relationship, what work plays to their actual edge— find scorecards easy to use\. Firms without an ICP find every bid "kind of a fit\." You can't read the label from inside the bottle\. The principal who has been running pursuits for fifteen years often can't see the firm's actual ICP without outside perspective\. The firm that says no this quarter has the capacity to pre\-position for a better bid two quarters from now— and pre\-positioning is where the real win rate lives\. ## Putting It Together: A Starting Path for Mid\-Market Civil Firms The 14\-point lift is three sequential moves: name the ICP, install the scorecard, then layer AI on the data you've started structuring\. Most firms try them in the wrong order\. 1. **Name the ICP\.** Who do you serve best? Where do you win on relationship, not price? This is strategic identity work, and it belongs to the principal\. 2. **Install the scorecard\.** Weight Fit, Risk, Profitability, Capacity\. Stand up the weekly committee\. Add the quarterly win/loss debrief\. 3. **Layer AI on the data\.** Once the scorecard has produced two or three quarters of consistent win/loss data, AI scoring becomes useful\. Before that, it's noise\. The right sequence is strategy → scorecard → AI\. Firms that buy the tool first end up using it to confirm bids they were going to pursue anyway\. A civil firm doesn't need a six\-figure BD overhaul to find the first 5–7 points of hit\-rate lift\. It needs one quarter of disciplined go/no\-go conversations using a simple weighted scorecard, and one principal willing to back the team when the team says no\. If the scorecard conversation is the one your firm has been avoiding, an outside view is often what unsticks it\. That's the work an [AI decision framework for founders](/blog/ai-decision-framework-founders) is built to support, and what [a fractional AI officer](/blog/what-is-a-fractional-ai-officer) does day\-to\-day\. More at [dancumberlandlabs\.com](https://dancumberlandlabs.com)\. ## FAQ Common questions civil\-firm principals ask before standing up a go/no\-go process and pairing it with AI\-assisted pursuit scoring: ### What is a good hit rate for a civil construction firm? The SMPS industry average for construction firms is 37\.9%, engineering firms 44\.2%, AEC overall 42%[1](/blog/blog-db-civil-construction-llc#ref-1)[2](/blog/blog-db-civil-construction-llc#ref-2)[3](/blog/blog-db-civil-construction-llc#ref-3)\. Firms with mature go/no\-go discipline often reach 50%\+, per the 2025 Unanet AEC Inspire Report[8](/blog/blog-db-civil-construction-llc#ref-8)\. A number above roughly 80% may signal underbidding rather than excellent selectivity\. ### What is a go/no\-go decision in construction bidding? A go/no\-go decision is a structured, scored evaluation of whether to pursue a specific RFP, typically weighted across Fit, Risk, Profitability, and Capacity[10](/blog/blog-db-civil-construction-llc#ref-10)\. Only 40% of AEC firms currently use a formal version[11](/blog/blog-db-civil-construction-llc#ref-11)\. The scorecard is most useful as a structured conversation, not as a replacement for principal judgment\. ### What does DB Civil Construction LLC do? DB Civil Construction, LLC is a DBE\-certified civil contractor in St\. Augustine, Florida, specializing in land clearing, underground utilities, roadway construction, and stormwater distribution[4](/blog/blog-db-civil-construction-llc#ref-4)\. The Florida LLC was filed on March 21, 2017 and remains active[5](/blog/blog-db-civil-construction-llc#ref-5)\. ### How does AI help civil contractors decide which bids to pursue? AI\-powered pursuit tools score opportunities against historical win patterns, flag compliance and risk gaps, and standardize the team's go/no\-go language[10](/blog/blog-db-civil-construction-llc#ref-10)\. More than half of A&E firms now use AI, primarily in business development and IT[12](/blog/blog-db-civil-construction-llc#ref-12)\. AI's first contribution at most mid\-market civil firms is structuring the win/loss data the model needs to be useful\. ### How long does it take to see hit\-rate improvement after introducing selectivity? Firms typically see noticeable improvement within two to four quarters, as declining lower\-fit pursuits compounds and capture work for better\-fit pursuits matures\. The first quarter is usually about defining the ICP and trialing the scorecard\. The second through fourth quarters are when win rates start to move\. ## Closing The firms quietly outperforming aren't the ones bidding the most\. They're the ones who have done the harder cultural work of naming the bids they won't pursue— and the strategic work of getting clear on who they serve best\. The 14\-point lift is plausible, not promised\. AI's role here is intellectual augmentation: helping the principal see what they couldn't see from inside the bottle\. ## References 1. Building Design \+ Construction Network, "How does your firm's hit rate stack up to the AEC competition?" \(2017\), citing SMPS Foundation — [https://www\.bdcnetwork\.com/how\-does\-your\-firms\-hit\-rate\-stack\-aec\-competition](https://www.bdcnetwork.com/how-does-your-firms-hit-rate-stack-aec-competition) 2. Building Design \+ Construction Network, "How does your firm's hit rate stack up to the AEC competition?" \(2017\), citing SMPS Foundation — [https://www\.bdcnetwork\.com/how\-does\-your\-firms\-hit\-rate\-stack\-aec\-competition](https://www.bdcnetwork.com/how-does-your-firms-hit-rate-stack-aec-competition) 3. SMPS Foundation, "Measuring for Success: A Look at Hit Rates & Other KPIs in the A/E/C Industries" \(2016\) — [https://cart\.smps\.org/store/hit\-rate\-report/692/](https://cart.smps.org/store/hit-rate-report/692/) 4. Dun & Bradstreet, "DB Civil Construction, LLC Company Profile" — [https://www\.dnb\.com/business\-directory/company\-profiles\.db\_civil\_construction\_llc\.ed36a001cdcc222b230c5204a2c0cafc\.html](https://www.dnb.com/business-directory/company-profiles.db_civil_construction_llc.ed36a001cdcc222b230c5204a2c0cafc.html) 5. Florida Department of State, Division of Corporations, "DB Civil Construction, LLC — Detail by Entity Name" — [https://search\.sunbiz\.org/Inquiry/corporationsearch/SearchResultDetail?inquirytype=EntityName&directionType=PreviousList&searchNameOrder=DBCIVILCONSTRUCTION\+L170000642570&aggregateId=flal\-l17000064257\-dfec7fdb\-13c9\-4876\-aab1\-f01794d82c8f&listNameOrder=DBCHS\+P040001622540](https://search.sunbiz.org/Inquiry/corporationsearch/SearchResultDetail?inquirytype=EntityName&directionType=PreviousList&searchNameOrder=DBCIVILCONSTRUCTION+L170000642570&aggregateId=flal-l17000064257-dfec7fdb-13c9-4876-aab1-f01794d82c8f&listNameOrder=DBCHS+P040001622540) 6. SMPS Foundation, "Measuring for Success: A Look at Hit Rates & Other KPIs in the A/E/C Industries" \(2016\) — [https://cart\.smps\.org/store/hit\-rate\-report/692/](https://cart.smps.org/store/hit-rate-report/692/) 7. JDB Engineering, "Stop Tracking Your Hit Rate the Old\-Fashioned Way\!" — [https://jdbengineering\.com/stop\-tracking\-hit\-rate\-old\-fashioned\-way/](https://jdbengineering.com/stop-tracking-hit-rate-old-fashioned-way/) 8. Unanet, "Half the Battle: Why AEC Firms Are Only Winning 50% of Bids" \(2025\) — [https://unanet\.com/blog/half\-the\-battle\-why\-aec\-firms\-are\-only\-winning\-50\-of\-bids](https://unanet.com/blog/half-the-battle-why-aec-firms-are-only-winning-50-of-bids) 9. Unanet, "Half the Battle: Why AEC Firms Are Only Winning 50% of Bids" \(2025\) — [https://unanet\.com/blog/half\-the\-battle\-why\-aec\-firms\-are\-only\-winning\-50\-of\-bids](https://unanet.com/blog/half-the-battle-why-aec-firms-are-only-winning-50-of-bids) 10. ContraVault, "Go/No\-Go Analyzer \| AI\-Powered RFP Bid Decision Tool" — [https://www\.contravault\.com/features/go\-no\-go\-analyzer](https://www.contravault.com/features/go-no-go-analyzer) 11. Unanet, "Half the Battle: Why AEC Firms Are Only Winning 50% of Bids" \(2025\) — [https://unanet\.com/blog/half\-the\-battle\-why\-aec\-firms\-are\-only\-winning\-50\-of\-bids](https://unanet.com/blog/half-the-battle-why-aec-firms-are-only-winning-50-of-bids) 12. OpenAsset, "Building the Future: How AI is Transforming the AEC Industry" \(2025\) — [https://openasset\.com/resources/ai\-in\-aec/](https://openasset.com/resources/ai-in-aec/) 13. OpenAsset, "Building the Future: How AI is Transforming the AEC Industry" \(2025\) — [https://openasset\.com/resources/ai\-in\-aec/](https://openasset.com/resources/ai-in-aec/) --- Source: https://dancumberlandlabs.com/blog/db-civil-construction-llc/