missionMATCH Platform Presentation

missionMATCH™Confidential

Notice

Confidentiality and Intellectual Property Notice

Read this notice before reviewing the material that follows.

This document and the platform it describes are confidential, proprietary, and protected. The methods, architecture, scoring model, data flows, and product design disclosed here are trade secrets and the intellectual property of the platform owner. They are shared solely for the purpose of evaluating a potential commercial relationship.

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Confidential internal evaluation by the named recipient. Review by the recipient's own advisors who are bound by equivalent confidentiality obligations.

Prohibited

Copying, redistribution, public posting, reverse engineering, or use of any concept, diagram, or method herein to build, specify, or procure a competing capability.

A full legal notice, including the confidentiality terms, the intellectual property reservation of rights, the no license grant, the demo data disclaimer, and the responsible use terms, appears at the end of this document. By continuing to read you acknowledge those terms.

Contents

01 Executive summary
02 The problem, and why it is hard
03 The four tools, one intelligence loop
04 How it works, step by step data flow
05 Platform data flow chart
06 The scoring engine, explainable by design
07 The AI brain, four augmentation classes over one engine
08 Architecture and technology
09 Responsible AI, compliance, and auditability
10 Security and deployment posture
11 Why we win, the commercial brief
12 Roadmap and integration readiness
13 Full legal notice and disclaimers

missionMATCH™01 Executive summary

Section 01

Executive Summary

missionMATCH turns a cleared workforce into a decision ready intelligence asset, from the moment a requirement lands to the moment a proposal ships.

Government contractors win or lose on people. A single unfilled key personnel line, a clearance that lapsed, or a slate that cannot survive an audit can sink a bid worth tens of millions. Yet most firms still run this on spreadsheets, tribal memory, and a resume folder. missionMATCH replaces that with a single platform that reads a requirement, scores every candidate against it with a transparent and defensible model, ranks a proposal ready slate, and keeps a complete compliance record, all under strict tenant isolation and validated cryptography.

Scoring dimensions

Purpose built tools

AI augmentation classes

Government clouds supported

What makes it different

Explainable by construction

Every match score is a transparent weighted breakdown with a human readable rationale and a recordable decision trace. This is an Automated Employment Decision Tool built to be defended, not a black box.

Compliance is a feature, not an afterthought

Demographic data is walled off from scoring entirely. The platform produces the adverse impact reporting a buyer needs for a NYC Local Law 144 or an OFCCP style review.

Built for the cleared world

Clearance hierarchy, poly, and compartment logic are first class. Cryptography is validated to the federal standard from day one. Cleared resumes are treated as controlled unclassified information.

No vendor lock in

The same system runs on Amazon Web Services GovCloud or Azure Government. Choosing a cloud is configuration, not a rewrite.

The one sentence version. missionMATCH is the system of record for the question every capture and staffing team asks daily, who can support this mission, and it answers that question in a way a compliance officer, a proposal manager, and a contracting officer can all trust.

missionMATCH™02 The problem

Section 02

The Problem, and Why It Is Hard

Cleared staffing is a matching problem wrapped in a compliance problem wrapped in a security problem.

The match is multidimensional

A qualified candidate is not just a skills match. It is the right clearance at the right level, the right certifications, prior experience with the customer agency, enough years, and the right place and availability. Miss one and the candidate is not proposal ready, no matter how strong the resume reads.

The stakes are asymmetric

A weak slate does not just lose a point, it can render a bid non compliant. Key personnel gaps, unsupported clearance claims, and stale availability are the quiet reasons good companies lose recompetes.

Every decision is an employment decision

Screening and ranking people for jobs is regulated. New rules on automated employment decision tools require that scoring be explainable, that a human stays in the loop, and that adverse impact can be measured. A tool that cannot show its work is a liability.

The data is sensitive

Clearance information and cleared resumes are controlled. They cannot be sprayed across consumer software as a service tools, logged carelessly, or sent to an unaccredited model endpoint.

The signal is scattered

The intelligence that decides a bid lives in five places at once, the resume, the requirement, the solicitation, the incumbent landscape, and the labor rate. Nobody has time to fuse it by hand before the proposal is due.

The gap in the market. Generic applicant tracking systems were built for commercial hiring at volume. They do not understand clearances, they do not fuse federal market signal, and they were never designed to defend an automated employment decision. missionMATCH is built for exactly this world.

missionMATCH™03 Four tools, one loop

Section 03

Four Tools, One Intelligence Loop

One shared engine and one shared data model power three focused tools, each answering a distinct question at a distinct moment in the capture lifecycle.

screenMATCH™

Is this candidate qualified for this requirement?

A single candidate qualification view. It produces the explainable score, the mission readiness panels, and the clearance intelligence for one person against one requirement. This is the desk level check a recruiter runs before advancing anyone.

slateMATCH™

Who belongs on the proposal candidate slate?

The core differentiator. It scores the whole pool against a requirement, ranks a proposal ready slate, and carries the human approval workflow and the adverse impact readout that a proposal can be defended with.

missionMATCH™

Who can support this mission today?

The workforce intelligence view. A command center that names the staffing gaps, quantifies the revenue at risk, surfaces upcoming opportunities and the incumbent landscape, and lets the whole team ask the AI brain a plain language question.

recruitMATCH™

Who is working the desk, and how is it going?

The delivery side. Clients, job orders and the hiring pipeline from first contact to placement, with the money modelled properly: a wrap rate, a real cost per hour, and margin reported separately from spread. Above it a manager view that scopes to a desk or a person, compares every figure against the window before it, bands recruiter load against the published benchmark, and turns a target into attainment.

The loop they serve

The four tools are stages of one loop. A requirement enters, candidate intelligence is gathered, the engine scores the match, a ranked slate is produced, and proposal readiness is recorded. The output of each stage is the input to the next, and every stage writes to the same audited system of record.

Requirement → Candidate Intelligence → Match Scoring → Ranked Slate → Proposal Readiness

Why one loop matters. Because the tools share an engine and a data model, the command center can name a gap and the candidates view can show the exact people who close it, telling one connected story rather than three disconnected reports.

missionMATCH™04 How it works

Section 04

How It Works, Step by Step

Follow a single requirement through the platform, from intake to a defensible proposal decision.

Intake the requirement

A requirement arrives, from a solicitation, a task order, or a capture plan. It carries a labor category, a required clearance level and poly, mandatory and desired skills, certifications, required years, a place of performance, and the customer agency. The platform stores it under the tenant, isolated from every other tenant by construction.

Gather candidate intelligence

Candidate profiles are ingested from resumes and connected systems. Parsing runs behind a provider interface so that cleared resumes, which are controlled unclassified information, are processed by an accredited or self hosted endpoint, never a consumer tool. Skills are resolved against a cleared skills ontology so that a synonym or an adjacent skill still matches.

Score the match, transparently

A pure, deterministic engine scores each candidate against the requirement across six weighted dimensions. The math is exact decimal arithmetic, so the same inputs always produce the same score, which is what makes the result auditable. Every score comes with a per dimension breakdown and a plain language rationale.

Rank the slate

For a requirement, the engine scores the whole eligible pool, ranks it, and assigns readiness bands. A learned re ranker can surface the candidates a reviewer is most likely to approve, but it only reorders, it never changes a score or a band. The frozen engine remains the single source of the number.

Keep a human in the loop

A person approves or rejects every slate member. The platform never auto rejects a candidate. Each decision is recorded with its rationale, so the slate carries its own defense.

Prove readiness and measure impact

The platform rolls the slate up into proposal readiness, and it computes selection rates and four fifths ratios so the customer can run a bias audit. Demographic data never touches the score, it lives in a separate, access controlled module used only for this reporting.

Fuse the market and ask the brain

Around the loop, the platform fuses real federal signal, open solicitations, historical awards, and labor rates, and exposes an AI brain that answers plain language questions grounded strictly in the tenant's own data, always citing its evidence and refusing anything it cannot support.

missionMATCH™05 Data flow chart

Section 05

Platform Data Flow Chart

How data moves from source, through the isolation and scoring layers, to a defensible decision and an immutable audit trail.

missionMATCH™06 Scoring engine

Section 06

The Scoring Engine, Explainable by Design

A pure, deterministic model is the floor of the whole platform. Everything else reads it and narrates it, nothing replaces it.

Dimension	Weight	How it scores
Clearance	25	Exact or higher than required scores full. A higher level satisfies a lower one, a lower level or an expired or unknown clearance scores zero. The hierarchy is encoded.
Skills	25	Full when all required skills are met. Exact match first, then semantic similarity for synonyms and adjacent skills, always recording which candidate skill matched which requirement.
Certifications	15	Required certifications present and current.
Customer experience	15	Prior experience with the requirement's customer agency.
Experience	10	Years of relevant experience against the required minimum.
Location and availability	10	Place of performance fit and current availability to staff.

Readiness bands

Green, proposal ready

Score 90 and above. Ready to place on a proposal.

Yellow, potential

Score 75 to 89. Viable with a gap to close.

Red, recruiting required

Below 75. A gap the team must source against.

Why deterministic decimal arithmetic. The engine uses exact decimal math, never floating point. The same inputs always yield the same score, to the digit. That bit stable repeatability is what lets a score stand as a defensible record months later, and it is why the scoring model can be an asset in a compliance review rather than a risk.

Explainability, built in

Every score carries a per dimension breakdown, so a reviewer sees exactly why a candidate landed where they did.
Every score carries a plain language rationale and a gap summary.
Every decision leaves a recordable trace, the raw material for an audit.
The engine is a pure function with no database and no side effects, exhaustively tested with golden and property tests. Any future learning layer sits on top of it, never in place of it.

missionMATCH™07 The AI brain

Section 07

The AI Brain, Four Augmentation Classes Over One Engine

The brain answers plain language questions, but it is not a chatbot bolted onto a database. It is a closed set of validated intents, each grounded, each audited, each unable to invent a number.

Retrieval augmentation

Answers narrative questions strictly from the tenant's own documents and profiles, citing the evidence it used. It narrates over retrieved records, it never scores.

Table augmentation

Answers questions that require the database to compute, a count, a sum, a distribution, by choosing from a curated set of safe, parameterized, read only queries. The model never writes a query itself.

Graph reasoning

Walks the relational graph already in the database, the skill adjacency, the market and incumbent landscape, the clearance lattice, returning a cited evidence path rather than an opinion.

Model augmentation

Serves calibrated learned signals, a win probability, a display order, a time to fill, computed from interpretable coefficients. By construction these can only emit an opportunity level number or an ordering, never a per candidate score.

The guardrails that make it safe

A closed registry. Every capability is a named, reviewed intent validated when the system starts. There is no free form path to the data.
A faithfulness gate. Generated prose ships only when it is confirmed to be supported by the evidence. Otherwise the platform returns the deterministic rendering of the underlying data, never a confident guess.
Refusal by design. A question about a protected attribute, or one the platform cannot ground, is refused, not answered.
Small cell suppression. Any count derived from people is suppressed below a safe threshold so an answer can never re identify an individual.
The engine stays the floor. No brain path recomputes or replaces the score. It reads what the engine decided and explains it.

What a user experiences. A capture lead types, who can support the intelligence analyst mission, or, what are our hardest staffing gaps, and receives a grounded answer with its citations, in seconds, without ever exposing a protected attribute or leaving the tenant boundary.

missionMATCH™08 Architecture

Section 08

Architecture and Technology

Chosen for cloud portability, a clean federal security story, and long term maintainability.

Backend

A single compiled, memory safe service in Go. One static binary, native validated cryptography, no heavy runtime to accredit. Thin handlers, logic in services, typed and compile checked database access.

Data

PostgreSQL with a vector extension for semantic search. Hybrid retrieval fuses full text and vector search. Schema changes only ever happen through reviewed migrations.

Tenancy

Postgres row level security in a pooled model. Every tenant scoped table enforces isolation with a using and a with check policy and forced row level security. No tenant context means zero rows, a safe default.

Frontend

A modern typed single page application, a left navigation shell, stat cards, dashboards, and the four tool surfaces, talking to the service over a first party authenticated session.

The layered design

Layer	Responsibility
Platform	Cross cutting concerns, configuration, database, tenancy, security, audit, storage, and the embedding and parsing providers, each behind an interface.
Engine	The pure deterministic scoring model, no input or output, the single per candidate scorer.
Modules	Each domain, matching, slates, command center, opportunity intelligence, compliance, auth, and the AI brain, as a self contained unit.
Interface	The HTTP API and the typed frontend that consume the modules.

Portability, a founding requirement

Everything cloud specific, object storage, the model provider, secrets, and identity, lives behind an interface. The platform runs on Amazon Web Services GovCloud or Azure Government with the other cloud's adapter present. Choosing a cloud is a configuration decision, not a code change, and there is no single vendor lock in on the core path.

Why Go and Postgres. The combination gives a single accreditable binary with validated cryptography and a portable, managed database on either government cloud. It is the cleanest path to a federal security posture without betting the product on one vendor's proprietary services.

missionMATCH™09 Responsible AI

Section 09

Responsible AI, Compliance, and Auditability

This is an Automated Employment Decision Tool, and it was designed from the first line to be defensible under scrutiny.

Explainable

Every match score is a per dimension breakdown plus a human readable rationale plus a gap summary plus a recordable decision trace.

Human in the loop

People approve or reject every slate member. The platform never automatically rejects a candidate.

Demographics walled off

Protected attributes live in a separate, access controlled module. They are never an input to scoring, and the scoring code cannot reach them.

Adverse impact reporting

The platform computes selection rates and four fifths ratios so a customer can run a NYC Local Law 144 or an OFCCP aligned review.

How the firewall is enforced

Demographic data lives in its own database schema reachable only by a dedicated compliance role. The application scoring role has no access to it at all.
The AI brain refuses any question that references a protected attribute or a close proxy, in plain words or in a query.
Adverse impact is computed through the compliance path only, and reporting on people is suppressed below a safe cell size so it cannot re identify anyone.
Every scoring and decision event is written to an append only audit record that carries no protected attribute and cannot be altered by the application.

An important honesty. The platform provides the data a customer needs for their own bias audit. It does not itself constitute the audit, and using it does not transfer the employer's legal responsibility. We state this plainly because a serious federal buyer expects that clarity.

Clearance is intelligence, not verification. Clearance values in the platform are self reported indicators shown as clearance intelligence. The authoritative check remains the hiring customer's facility security officer through the government system of record at onboarding. The platform states this once wherever clearance appears, so no one mistakes an indicator for a verification.

missionMATCH™10 Security and deployment

Section 10

Security and Deployment Posture

Aligned to the federal control baseline, with validated cryptography enabled from day one.

Validated cryptography

The service is built with the native validated cryptographic module enabled. Passwords use a standards based key derivation with a high iteration count and a server held pepper. Secrets at rest are encrypted with authenticated encryption, with versioned keys so they can rotate.

Authentication

First party opaque server side sessions in a hardened cookie, instantly revocable, with mandatory time based one time password multifactor implemented on the validated standard library. No path yields an active session with multifactor unsatisfied.

Access and identity

Role based access, one human to one account to one organization, provisioning and authority bounded invites rather than open self signup, and federated single sign on kept behind a clean interface for when a customer needs it.

Data protection

Controlled unclassified information is never logged. Cleared resumes are parsed by an accredited or self hosted endpoint. Transport is encrypted, and every export is logged.

Delivery

Delivered as containers with infrastructure as code, targeting both government clouds, with a software bill of materials produced at build.
Row level security, append only audit, and least privilege database roles are structural, not optional configuration.
A continuous integration gate fails closed on any attempt to reintroduce a development only bypass into a production build.

Accreditation is a shared journey. The platform is engineered to the federal baseline and uses validated cryptography, which shortens the path. A specific authorization to operate in a customer environment is a joint effort with the customer's security team, using the customer's accredited cloud and endpoints.

missionMATCH™11 Why we win

Section 11

Why We Win, the Commercial Brief

The value is measured in bids won, hours saved, and audits passed.

The value we create

Win more

Stronger, faster, defensible slates. The command center names the gap and the platform shows the exact people who close it, before the proposal is due.

Spend less effort

The hours a team burns fusing resumes, requirements, and market signal by hand collapse into a single scored view.

Carry less risk

Every employment decision arrives with its own explanation and audit trail, and the adverse impact reporting a review needs.

Who buys, and why

Buyer	Their pain	What the platform gives them
Capture and proposal teams	Slates assembled under deadline, on instinct	A ranked, proposal ready slate with a defensible rationale in minutes
Cleared staffing firms	Matching cleared talent to reqs at scale	A screening and slate engine that understands clearances natively
Program and workforce leads	No live picture of staffing gaps and risk	A command center that names gaps and quantifies revenue at risk
Compliance and legal	Exposure on automated employment decisions	Explainability, a human in the loop, and adverse impact reporting

Our defensible advantages

Purpose built for cleared work

Clearance, poly, and compartment logic and controlled data handling are first class, not retrofitted onto a commercial applicant tracking system.

Compliance as a moat

An explainable, auditable, firewall enforced decision tool is hard to copy and exactly what a regulated buyer requires.

One engine, one story

Four tools over one engine and one data model tell a single connected story instead of four disconnected reports.

Portable and future proof

Both government clouds, no lock in, and a learning layer that improves ranking over time without ever compromising the defensible core.

missionMATCH™12 Roadmap

Section 12

Roadmap and Integration Readiness

The core loop is built and running. The next horizon deepens the intelligence and connects the systems a customer already owns.

Live today

Explainable scoring engine screenMATCH slateMATCH missionMATCH command center AI brain, four augmentation classes Capture readiness decision Vendor intelligence Adverse impact reporting Multi tenant isolation Validated cryptography and authentication Federal market signal fusion

Learning layer, advancing

Calibrated win probability, a display order re ranker, and a time to fill forecast, each learned from the tenant's own outcomes, each governed by a quality and a fairness gate before it is ever promoted, and none of which can replace the deterministic per candidate score.

Integration ready, activated with the customer

The following connect a customer's existing systems and licensed data. Each is engineered and waiting only on the customer's own credentials and grants, which is the honest status, not a limitation of the platform.

Integration	Activated by
Applicant tracking system ingestion	The customer's system credentials and tenant grant
Human capital and document systems	The customer's tenant scoped access grant
Licensed opportunity intelligence feeds	The customer's data license
Production deployment to a government cloud	The customer's accredited cloud account
Self hosted accredited model endpoint	The customer's provisioned secure endpoint

The engagement model. A pilot begins on synthetic data to prove the loop and the workflow, then connects the customer's own systems and deploys into the customer's accredited environment. The platform is ready for that on day one.

missionMATCH™13 Legal notice

Section 13

Full Legal Notice and Disclaimers

1. Confidentiality

This document and all information it contains are confidential and proprietary to the platform owner. It is provided to the named recipient solely to evaluate a potential business relationship. The recipient shall not disclose it to any third party except to its own professional advisors who have a need to know and who are bound by confidentiality obligations at least as protective as those stated here, shall protect it with no less than a reasonable degree of care, and shall return or destroy it on request. These obligations survive the end of any discussions between the parties.

2. Intellectual property, reservation of rights

The platform, its architecture, its scoring methodology, its data flows, its user interfaces, its documentation, and the names missionMATCH, slateMATCH, and screenMATCH, together with all associated designs, know how, and trade secrets, are the exclusive intellectual property of the platform owner and are protected by applicable trade secret, copyright, trademark, and other laws. All rights are reserved.

3. No license and no transfer

Nothing in this document grants, by implication, estoppel, or otherwise, any license or right to any patent, copyright, trademark, trade secret, or other intellectual property of the platform owner. No right to make, use, reproduce, modify, distribute, reverse engineer, or create derivative works of the platform or any part of it is conveyed. The recipient shall not use any concept, method, diagram, workflow, or design element disclosed here to design, build, specify, procure, or assist a third party in building a competing or similar capability.

4. No warranty

This material is provided for informational purposes only, on an as is basis, without warranty of any kind, express or implied, including any warranty of merchantability, fitness for a particular purpose, accuracy, or non infringement. Statements about capabilities, roadmap, and performance are forward looking and subject to change without notice, and are not a commitment, guarantee, or contract.

5. Demonstration and sample data

Any candidate, personnel, clearance, opportunity, award, vendor, or organization data shown in a demonstration or in this material is synthetic sample data generated for illustration. It does not represent real individuals or real clearance determinations. Clearance indicators in the platform are self reported and are presented as clearance intelligence, not as verification. The authoritative clearance determination is made by the hiring customer's facility security officer through the government system of record at onboarding.

6. Responsible use and no legal advice

The platform is an aid to human decision making. It is designed to keep a qualified human in the loop for every employment decision and it never automatically rejects a candidate. It provides data to support a customer's own bias and compliance audits, but it does not itself constitute a legal audit, a compliance certification, or legal advice, and its use does not transfer or discharge the employer's legal responsibilities under applicable employment, civil rights, or contracting law. The customer remains responsible for its own compliance obligations.

7. Third party marks

Any third party names, cloud provider names, agency names, or standards referenced are the property of their respective owners and are used for identification only. Their use does not imply endorsement, affiliation, or sponsorship.

8. Acknowledgement

By reviewing this document the recipient acknowledges and agrees to the terms of this notice. If the recipient does not agree, the recipient must not review, copy, or use this document and must return or destroy all copies.