An independent 30-point assessment across 8 evaluation domains of contractor and subcontractor AI and software readiness for federal agency awards.
The Encore Federal AI Readiness Audit (EFAIRA) produces a color-coded scorecard across eight domains, identifies gaps with a baseline of what good looks like, and delivers a signed hash-verifiable Encore Readiness Receipt that contractors can cite in their technical proposal volume. All intake submissions are protected under a mutual non-disclosure agreement.
A defensible third-party signal that did not exist for federal AI procurement.
Federal agencies evaluate more AI software than ever and have fewer trusted technical authorities to validate what is being proposed. Vendor pitches exceed the procurement team's ability to independently evaluate. Compliance frameworks cover security but not AI-specific risk. Existing third-party assessors are either compliance-checklist firms with no AI engineering capability or large consultancies that quote at price points incompatible with most pursuits.
The EFAIRA fills the gap between vendor pitch and procurement decision with a defensible 30-point assessment a contractor can run in weeks at a price point compatible with their pursuit economics.
- Adaptive Compound Intelligence (ACI), the patent-pending federal AI intelligence substrate that anchors Encore’s engineering practice.
- Oxford University AI Program Top 1 percent of cohort. Founding ACI work reviewed by faculty as a fundamentally new category of intelligence architecture.
- SDVOSB, VOSB, SDB, MBE verified. Veteran-founded since 2010 with a sixteen-year continuous federal operating record.
- Active federal AI patent portfolio covering contract writing, procurement decision support, and the underlying intelligence substrate.
Thirty points across eight evaluation domains.
Each point is scored zero to one hundred percent with a color-coded readiness indicator. Red below forty percent. Yellow between forty and eighty percent. Green at eighty percent and above. Every score is supported by documented evidence and a one-sentence finding.
Mission Fit and Outcomes
How sharply the proposed system maps to the agency's stated mission objectives, whether claimed outcomes can be traced to measurable signals, whether the proposal pitches the evaluator with the right hook in the right place, and whether the system still makes sense three to five years out.
Mission alignment
Software maps cleanly to the agency's stated program objective. Scope is bounded to the mission, not broadened to fit the procurement.
Outcome traceability
Every claimed outcome maps to a measurable instrumented signal. No outcome is asserted without an evidence path.
Use case boundary clarity
Use case is sharply defined. Not 'AI for everything in your agency.' Bounded, defensible, scoped.
Operational embedding
Integration into the agency's existing workflow is designed and documented. Not implied. Not assumed.
Evaluator hook strength
Proposal pitches the evaluator in the first paragraph. Hook is on page one, not on slide seventeen.
Three to five year viability and strategic durability
Will the proposed system still make operational sense three to five years from now? Does it survive plausible AI market evolution, agency mission drift, and adjacent capability obsolescence? Or is it a point-in-time bet that locks the agency into a tool that does not age well.
Codebase and Architecture Readiness
Whether the underlying engineering can survive scrutiny from a federal CIO technical reviewer. Production-grade vs. proof-of-concept dressed up as product. Defensible vs. fragile.
Codebase maturity
Production-ready codebase with test coverage, CI/CD discipline, and observable engineering practice. Not a proof-of-concept rebranded as a product.
Architecture defensibility
Architecture survives scrutiny from a federal CIO's technical reviewer. Decisions are documented and justified.
Dependency lineage
Open-source, foundation model, and third-party dependencies are inventoried and clearable for federal use. License obligations satisfied.
Deployment readiness
System can actually deploy into the agency's cloud or on-premises environment. Not a demo-only setup that breaks at deployment.
AI Specifics
The deepest evaluation domain. Model lineage, grounding, drift, fallback, confidence scoring, self-improvement, and self-healing. The seven AI-specific points that close the gap between proposal claims and operational reality. GAO-26-107859 identified these exact concerns as agencies' top AI acquisition risks.
Model lineage and data provenance
Origin of the underlying models. Training data composition. License compliance for federal use. Supply chain transparency end to end. GAO-26-107859 cites vendor training data quality as a top federal AI acquisition concern.
Hallucination posture and grounding
How the system handles cases where it does not know the answer. Whether outputs are grounded, sourced, and verifiable against authority.
Drift detection and human-in-the-loop
Whether the system detects its own degradation over time. Where human-in-the-loop checkpoints exist and how they trigger. GAO-26-107859 cites model performance degradation as a top federal AI acquisition risk.
Fallback and failure-mode design
What happens when the AI is wrong, slow, or unavailable. Whether mission-critical workflows degrade gracefully or stop.
Confidence scoring and uncertainty quantification
Does every AI output carry a confidence score or uncertainty interval? Can evaluators tell when the system is sure versus guessing? Confidence transparency is the single largest gap between consumer AI and federal-grade AI.
Self-improvement and documented update plans
Does the system get better with use, or does it plateau? Is there a documented model refresh cadence, capability evolution roadmap, and patch discipline? Federal sustainment depends on knowing how the system evolves over the contract period.
Self-healing and resilience design
Does the system detect its own degradation and route around it automatically, or does failure cascade to operations before a human catches it? Production-grade resilience separates federal-ready systems from demos that survive only happy paths.
Security and Compliance
Boundary controls, framework alignment, host environment posture, and the audit trail that lets oversight reconstruct every AI decision and human approval. Federal-grade or not, scored against NIST, FedRAMP, CMMC, and federal data residency requirements.
Boundary controls and data flow
What federal data crosses the system boundary. Controls are documented, testable, and aligned to applicable frameworks.
NIST, FedRAMP, and CMMC alignment
Authorization-readiness position. NIST 800-53 mapping. FedRAMP and CMMC posture at the level required by the procurement. NIST AI Risk Management Framework alignment, including the April 2026 Trustworthy AI in Critical Infrastructure profile where applicable.
Audit trail and tamper-evidence
Every AI decision and every human approval can be reconstructed for oversight. Tamper-evident logging in place. Audit posture sufficient for OMB M-25-22 routine inspection.
Data residency, host environment, and tenancy isolation
Where federal data physically resides. AWS GovCloud, Azure Government, or commercial cloud posture. FIPS 140-2 compliant hardware. CONUS residency where required. U.S. persons administration where applicable. Multi-tenancy isolation that prevents customer data from leaking into vendor model training.
FedRAMP authorization level alignment to procurement value
Is the contractor's FedRAMP authorization at the level the procurement actually requires? Low when High is required is a procurement-blocker. The contractor's authorization path must be realistic for the contract timeline, not aspirational.
Proposal and Procurement Posture
Narrative coherence across the technical volume and past performance that actually matches the requirement. The two procurement-side gaps that contractors most often miss in technical proposals.
Proposal narrative coherence
Proposal tells one coherent story across the technical volume. Not six disconnected sub-stories stapled together by section dividers.
Past performance match
Stated past performance actually matches the work being proposed. Not adjacent work dressed up to look applicable.
Sustainment and Lifecycle
GAO-26-107859 (April 2026) identified federal AI sustainment as a leading acquisition concern. This domain scores whether the proposed system can survive the full contract period under the contractor's stated support model. Three points evaluated independently of the technical evaluation.
Vendor evergreen and update cadence
Patch discipline. Model refresh cadence. Capability evolution roadmap. Incident response posture. How the vendor commits to evolving the system over the contract period, with documented evidence rather than marketing assertions.
Agency sustainment posture and knowledge transfer
Can the agency operate the system if the vendor relationship changes? Is there a documented knowledge-transfer plan? Are the artifacts the agency would need to sustain the system identified, scoped, and committed in the proposal?
Lifecycle total cost and renewal economics
Total cost of ownership over five years. Hidden cost surfaces at scale. Renewal economics realistic against the agency's likely budget posture. Cost-curve transparency rather than back-loaded pricing that catches the agency off guard.
Federal AI Governance Alignment
Whether the proposed system supports the agency's obligations under OMB M-25-22, OMB M-24-10, and Executive Order 14110. Federal agencies are required to implement minimum risk management practices for high-impact AI within 365 days of acquisition. Systems that do not support that obligation create downstream compliance risk for the agency.
Minimum risk management practices alignment (OMB M-25-22)
Does the proposed system support the agency's obligation to implement minimum risk management practices under OMB M-25-22? Risk-tier classification clarity, documented controls, instrumented monitoring, and incident posture aligned to the OMB framework.
High-impact AI classification readiness
If the proposed system meets the OMB high-impact AI threshold, is the documentation ready for the 365-day post-acquisition obligation? Inventory artifacts, Chief AI Officer designation alignment, and routine inspection readiness as required by OMB M-24-10.
Public-Trust and Mission Ethics
The final domain. Whether AI-influenced decisions can be explained to the public and contested by a citizen. Public trust is the ultimate constraint on federal AI deployment. A system that cannot answer 'why did the AI say that' to a citizen will be defunded politically faster than it fails technically.
Explainability, contestability, and public-trust readiness
Can decisions influenced by the AI be explained to the public in plain language? Can a citizen contest an AI-influenced decision through an established process? Are explainability mechanisms documented, tested, and aligned to applicable AI governance frameworks? This is the point where mission ethics meets operational reality.
The methodology cites the federal guidance agencies are required to evaluate against.
Every domain in the EFAIRA maps to a current federal AI procurement framework or recent oversight finding. The methodology is built to score the exact concerns federal evaluators have been directed to ask about.
Artificial Intelligence Acquisitions
GAO identified vendor training data quality and model performance degradation as top federal AI acquisition risks. EFAIRA scores both concerns directly in Domain III (AI Specifics) and Domain VI (Sustainment and Lifecycle).
Procuring AI Systems and Services
OMB guidance to federal agencies on procuring AI responsibly and cost-effectively. EFAIRA scores compliance support across Domain IV (Security and Compliance) and Domain VII (Federal AI Governance Alignment).
Advancing Governance, Innovation, and Risk Management
OMB framework requiring AI governance structures, system inventories, Chief AI Officer designations, and minimum risk management practices for high-impact AI. EFAIRA scores agency obligation support in Domain VII.
AI Risk Management Framework + Trustworthy AI in Critical Infrastructure Profile
NIST guidance on AI risk practices, including the April 2026 Trustworthy AI in Critical Infrastructure profile. EFAIRA Domain IV scores alignment to NIST 800-53 controls and the AI RMF where applicable.
Federal Security Baselines
Authorization frameworks for federal cloud, information systems, and defense industrial base. EFAIRA scores authorization-readiness, data residency, and tenancy isolation across Domain IV.
Federal Acquisition Regulation and Defense Supplement
Procurement integrity, past performance evaluation criteria, and proposal requirements. EFAIRA scores proposal narrative coherence and past performance match in Domain V (Proposal and Procurement Posture).
Six deliverables. Federal-grade voice. Defensible by design.
Executive Summary
One page. Overall readiness percentage. Top three strengths and top three gaps. Briefable in five minutes to a CEO, a CIO, or a contracting officer.
30-Point Scorecard
Color-coded grid. Percentage per point. One-sentence finding per point. The full scorecard delivered as both a visual artifact and a structured data export.
Domain Briefings
Five briefings, one per domain. What was scored, why, and what evidence was reviewed. Each briefing is signed by the Encore engineering team that produced it.
Gap Baseline Memo
For every red or yellow point: what is missing, what good looks like, suggested fix approach calibrated to your visible technology stack, and approximate effort to close. Suggestions only. Liability disclaimed.
Encore Readiness Receipt
Issued only when overall score is 80 percent or higher. Signed, dated, hash-verifiable. Citation language included for the technical volume of your proposal.
Follow-Up Engagement Offer
Delivered seven days after the report at the contractor's option. Two engagement paths offered: targeted gap hardening under defined Statement of Work, or ongoing pursuit partnership through proposal submission.
Different problems. Different price points.
EFAIRA fills the specific gap between vendor pitch and procurement decision. Most alternative paths solve a different problem, take longer, and cost more because they are built for a different scope.
| Path | Price | Timeline | Built To Score | Different From EFAIRA |
|---|---|---|---|---|
EFAIRA Snapshot (Encore) | $15K–$25K | 2 weeks | All 30 points across 8 domains. Color-coded scorecard. Gap baseline memo. Signed hash-verifiable Readiness Receipt for technical volume citation. | — |
EFAIRA Standard (Encore) | $45K–$85K | 4–6 weeks | All Snapshot deliverables plus codebase review, AI capability bench testing against the procurement use case, and detailed domain briefings. | — |
Big-4 federal AI strategy engagement | $250K+ | 3–6 months | Broad strategic advisory across the contractor's full federal pursuit roadmap. Billable-hour model with senior partner oversight. | Built for enterprise-wide strategy. Not built to produce a citable pre-award AI readiness scorecard at the pursuit level. |
FedRAMP 3PAO assessment (Moderate baseline) | $350K–$2M | 12–18 months | Cloud-system security authorization to a defined FedRAMP baseline. Required for hosting federal data above specific risk thresholds. | Scoped to security authorization only. Does not score AI specifics, mission fit, proposal narrative, or pre-award procurement posture. |
Compliance-checklist boutique | $25K–$75K | 4–12 weeks | NIST and FedRAMP gap analysis. Generic AI questionnaire coverage. Compliance posture documentation. | Built for compliance posture, not AI-specific risk. No citation alignment to GAO-26-107859, OMB M-25-22, or NIST AI RMF. No hash-verifiable receipt. |
GovTech bid and capture consultancy | $40K–$120K | 4–8 weeks | Bid/no-bid analysis, capture strategy, win-theme development, color-team review of proposal volumes. | Built for proposal narrative and win strategy. Does not score AI codebase maturity, AI model lineage, drift posture, or technical AI readiness. |
Pricing and timeline ranges reflect publicly reported figures and Encore's market analysis as of 2026. Big-4 figures reflect typical federal advisory engagements. FedRAMP figures reflect fedrampcost.com market data for Moderate authorization. Boutique and GovTech ranges reflect typical SDVOSB peer-market posting and public proposal data. EFAIRA is a pre-award assessment service and does not substitute for a FedRAMP authorization or a procurement protest defense.
Scaled to procurement weight and decision criticality.
Three engagement tiers. Each tier produces the same defensible methodology, scaled to the depth required by the pursuit.
Snapshot
Small primes and subs pursuing federal tasks under $5M.
- 30-point scorecard with color-coded readiness grid across 8 evaluation domains
- One-page executive summary
- Top three strengths and top three gaps
- Gap baseline memo with suggested fix approaches
- Encore Readiness Receipt if overall score is 80 percent or higher
Standard
Primes pursuing federal awards in the $5M to $50M range.
- Full Snapshot deliverables
- Codebase review with deeper architecture inspection
- AI capability bench testing against the procurement use case
- Five domain briefings with detailed evidence review
- Encore Readiness Receipt with citation language for technical volume
Enterprise
Large contractors on $50M and above pursuits.
- Full Standard deliverables
- Side-by-side comparison against two or three named competitors where public data permits
- Agency-specific positioning recommendations
- Direct working sessions with Encore's CTO and engineering leadership
- Encore Readiness Receipt plus full citation package
Mutual NDA at intake. IP carve-outs. Conflict-of-interest screen.
Every submission to the EFAIRA process is protected under a mutual non-disclosure agreement, signed at the intake portal before any artifact is reviewed. The NDA explicitly bars Encore from developing competing offerings using submitted intellectual property and gives the contractor full retention of all IP submitted for assessment.
Encore operates a hard conflict-of-interest screen at intake. Encore does not assess contractors pursuing awards Encore is also pursuing under the same solicitation. Where a conflict is identified, the engagement is declined before any artifact is reviewed.
- Encore identifies gaps. Encore suggests fix approaches based on the contractor's visible technology stack. Encore does not implement fixes during the assessment phase.
- Hardening engagements that remediate identified gaps are scoped separately under a defined Statement of Work, executed after the audit is delivered.
- Partnership conversations are scheduled no sooner than seven days after report delivery, never during the assessment phase.
- Every Readiness Receipt carries a point-in-time disclaimer and scope limitation. Receipts reflect submitted artifacts at assessment and do not warrant future performance.
Pursuing a federal award? Run the audit first.
Initial scoping conversations are confidential. Tell us about the pursuit, the agency, and the proposed system. Encore returns a tier recommendation and a fixed engagement quote within five business days.