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One digital twin. Three ways to use it.

Every module reads from the same evidence-backed map of your firm's capabilities. Score a pursuit, question the roster, or price the risk: it is one twin, answering three ways.

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Opportunity Intelligence

Pursuit readiness, before you commit

Feed Atlas the RFP. In seconds it reads the requirements, matches them against your firm's actual delivery record, and tells you whether you can staff the work you are about to chase.

The answer is never a black box: every readiness score comes with the drivers behind it, cited to the evidence they came from.

  • A readiness score with cited drivers
  • A recommended team pulled from your own bench
  • A gap-closure plan with hire, subcontract, and upskill timelines
Atlas Opportunity Intelligence: a pursuit scored for readiness with recommended team and gap plan
Roster Intelligence

Ask your organization anything

“Who has run a hospital job over $50M?” “Who else can stamp drawings in Nevada?” Ask in plain language and Atlas answers from the digital twin, with the evidence attached.

It also tells you what you didn't ask: where one resignation would take a capability to zero, and where credential coverage is thinner than the org chart suggests.

  • Plain-language questions, cited answers
  • Single points of failure flagged before they bite
  • Credential and license coverage across every employee
Atlas Roster Intelligence: a plain-language question answered with cited evidence
Portfolio Coverage

Capability gaps, priced in dollars

A gap is an abstraction until it has a price. Portfolio Coverage connects your capability map to your client relationships and quantifies what is actually at stake.

Leadership sees revenue at risk, the cost to protect it, and which client relationships depend on capabilities the firm holds thinly.

  • Revenue at risk, by client relationship
  • The cost to protect it: hire, subcontract, or upskill
  • What-if scenarios run against the live roster and pipeline
The delivery gap

You're on track to win $29M of work you can't staff.

5
expertise short
$29M
at risk
$400K
to protect it
Client readinessRevenue exposed
  • Confidential hyperscale developer75GOOD$12.5M
  • Mid-Atlantic private university83STRONG$8.5M
  • Brooklyn residential developer100STRONG$5.2M
  • NJ schools development authority86STRONG$2.8M

Weighted across six active client relationships, recomputed as the roster and pipeline change.

Construction crew working on an active jobsite at golden hour
The work

From pursuit to mobilization: know the team before you commit the schedule.

How Atlas thinks

AI finds the evidence. Math does the scoring.

No black boxes. Atlas's AI gathers and cites the evidence; deterministic scoring does the judgment.

In practice that means the language model never decides your readiness score. It reads public records, extracts claims, and attaches sources. Then deterministic math, the same math every time, turns that evidence into scores you can defend in a go/no-go meeting.

Cited, always

Every answer links to the evidence behind it, 100% of the time.

Corroborated 3×

Nothing locks until three independent sources agree.

Deterministic scoring

Readiness scores come from transparent math, not model mood.

Instant answers

Seconds from question to answer, across your whole roster.

Questions

Asked by every firm we talk to

Nothing, to start. Atlas builds your initial capability map from published project records, so the first version of your firm's twin exists before any IT conversation happens. When you're ready, you phase in pipeline and utilization data from the systems you already run.

From evidence. Atlas reads the published record of your firm's work: project filings, licensing boards, firm publications, and maps each colleague to the work they can actually deliver. Every claim is cited to its source, and nothing locks into the twin until three independent sources agree.

You correct it, and the correction sticks. Your team reviews the map, confirms what's right, and fixes what isn't; Atlas treats your corrections as ground truth from then on. The map gets sharper with every review cycle.

No. Your data is used to answer your firm's questions and nothing else. There is no model training on customer data, and every workspace is isolated with row-level security.

AI finds the evidence. Math does the scoring. The AI's job ends at gathering and citing evidence; readiness scores are computed by transparent, deterministic math, so the same inputs always produce the same score, and you can always see why.

The first capability map is built from public records before you lift a finger, so the first demo is about your firm, not a generic sandbox. From there, monthly review cycles sharpen the twin, and each phase ships a concrete deliverable.

Pricing is discussed on the demo. It depends on firm size.

Never wonder if your bench can execute its future.

We build your firm's twin before the first call. Bring a pursuit, and leave with your readiness score, the gaps, and the plan to close them.

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