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Systems built around real business problems.

Explore how Stakforge has helped client teams connect data, automate operations, build intelligent products, and create a durable path from idea to production.

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Revenue cycle / decision intelligenceOne performance story. Three decision lenses.
Revenue-cycle decision fieldFour source domains converge in a governed claims-and-payments model; executive, manager, and analyst views branch from it, with feedback from decisions to operations.PAYER PORTALSPM SYSTEMSCLEARINGHOUSEWORK QUEUESSHARED METRICSClaims +paymentsEXECUTIVEPortfolioMANAGERQueue actionANALYSTRoot cause
Illustrative system model / anonymized engagement
Healthcare RCM companyHealthcare, revenue cycle

A full-suite analytics platform for revenue cycle management

Unified dashboards, governed metrics, and ML-powered early warning signals gave an RCM company a single performance story for both internal leaders and client reporting.

Analytics PlatformsHealthcareRevenue Cycle
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Healthcare payer / typed ontologyTrace the feature back to the entity.
Payer feature lineageA member-centered ontology links coverage, claims, providers, and encounters to three candidate feature families, then applies quality and access controls before authorized use.COVERAGECLAIMMEMBERENCOUNTERPROVIDERUTILIZATIONRISKCARE GAPSQUALITY + ACCESS GATEAUTHORIZED TEAMS
Illustrative system model / anonymized engagement
Regional healthcare payerHealthcare, payer

Building an ML-ready data foundation for a healthcare payer

Conformed entities, reusable features, and hardened pipelines reduced model setup friction and increased trust in analytics across regulated healthcare workflows.

Data EngineeringHealthcareML Foundation
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INSURANCE / DATA LANDSCAPEFrom fragmented streams to governed domain productsIllustrative model / anonymized engagement
Insurance data as a governed landscapeIllustrative, anonymized insurance lineage. Policy administration, claims, billing, and broker sources enter incremental capture. A canonical model relates policy, coverage, exposure, claim, and reserve. Tests, freshness monitoring, and release approval govern reusable data products for underwriting, claims, actuarial, and finance. Data operations use alerts, runbooks, promotion, and rollback.01 / FRAGMENTED SOURCES02 / CANONICAL INSURANCE MODEL03 / GOVERNED RELEASEPolicy adminClaimsBillingBrokerINCREMENTALCAPTUREPolicyCoverageExposureClaimReservehashas claimcovershas reserveQUALITY GATEtest · freshnessrelease approvalUnderwriting1Claims2Actuarial3Finance4REUSABLE DOMAIN PRODUCTSOPERATIONS LOOPFreshness · schema drift · failed dependenciesRELEASE DISCIPLINERunbooks · environment promotion · rollbackLINEAGE: SOURCE → ENTITY → TESTED PRODUCT → DECISION USEInsurance source-to-product lineageIllustrative, anonymized insurance lineage. Policy administration, claims, billing, and broker sources enter incremental capture. A canonical model relates policy, coverage, exposure, claim, and reserve. Tests, freshness monitoring, and release approval govern reusable data products for underwriting, claims, actuarial, and finance. Data operations use alerts, runbooks, promotion, and rollback.SOURCESONTOLOGYPRODUCTSPPolicyCClaimsBBillingRBrokerMODELPolicyClaimTEST + RELEASEUnderwritingActuarialOBSERVE FRESHNESS / DRIFT / FAILED DEPENDENCIES
Trace / source to entityControl / test and releaseUse / shared domain products
Regional commercial insurerInsurance

Shipping a modern data platform for a commercial insurer

A modern ingestion, transformation, and observability stack replaced brittle extracts with a durable policy and claims data foundation.

Data EngineeringInsuranceLakehouse
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FINANCE / CONTROLLED EXCEPTIONSThe machine gathers; policy and people decideIllustrative model / anonymized engagement
Controlled financial exception decision loopIllustrative, anonymized financial operations state machine. Ledger, partner, and settlement exceptions are classified. Tools retrieve ERP and ledger records, partner API responses, policy and procedure references, and prior resolutions. A structured proposal passes amount, account, and timing policy checks. Material or sensitive actions route to analysts for approval or revision; permitted actions are bounded. Tool, model, policy, and applicable human decisions are recorded for audit.SIGNAL / RECONCILIATIONTOOL-AUGMENTED EVIDENCEPOLICY-BOUNDED DECISIONLedgersPartner feedsSettlement01 / DETECTExceptionEVIDENCE02 / GATHERPROPOSAL03 / CLASS + NEXT STEPPOLICY04 / CONTROLERP + ledgerPartner APIPolicy / SOPPrior casesamount · account · timingANALYSTMATERIAL / SENSITIVEapprove · reviseACTIONBOUNDED RESPONSEwithin policyrevision returns to proposalAUDIT EVENT TRAILException classTool responsesModel versionPolicy resultApproval** HUMAN APPROVAL RECORDED WHEN REQUIRED; ACTIONS REMAIN POLICY-BOUNDED.Supervised exception pathIllustrative, anonymized financial operations state machine. Ledger, partner, and settlement exceptions are classified. Tools retrieve ERP and ledger records, partner API responses, policy and procedure references, and prior resolutions. A structured proposal passes amount, account, and timing policy checks. Material or sensitive actions route to analysts for approval or revision; permitted actions are bounded. Tool, model, policy, and applicable human decisions are recorded for audit.EXCEPTION → EVIDENCE → POLICY → ACTION01 DETECT02 GATHER03 PROPOSE04 GATEANALYSTACTIONTOOL · MODEL · POLICY · HUMAN DECISION TRACE
Machine / collect and proposeControl / policy before actionAuthority / analyst on material risk
Mid-market financial services firmFinancial operations

Agentic LLM workflows for financial reconciliation and exceptions

Structured exception taxonomies, tool-augmented agents, and approval workflows reduced cycle times while preserving auditability for finance operations.

LLM SystemsFinanceAgentic Workflows
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CLINICAL INTAKE / GOVERNED AUTOMATIONA protected document becomes a decision-ready referralIllustrative model / anonymized engagement
Protected clinical intake, extraction, and reviewFaxes, referral packets, authorization requests, and secure messages cross into a controlled PHI environment. Document classification and schema-constrained extraction produce fields for member, provider, authorization, and scheduling. Validation and risk-confidence rules send complete low-risk items to EHR and work queues, while ambiguous or high-risk items go to clinician review. Audit events and corrections support evaluation.Clinical document intake with human review forkInside a PHI-controlled environment, a referral is read into structured fields. A risk and confidence decision routes complete low-risk work to an EHR queue or ambiguous work to clinician review. The decisions are audited.PHI-CONTROLLED INTAKEFAX · PDF · MESSAGEReferralMemberProviderAuthorizationSchedulingRISK +CONFIDENCEEHR queueClinicianEXTRACT → VALIDATE → ROUTE · AUDIT EACH DECISIONDetailed governed referral intake on mobileA private intake boundary contains document OCR and classification, constrained field extraction, schema and consistency validation, a risk-confidence branch to EHR queues or clinician review, and an immutable audit trace.PHI BOUNDARY / CONTROLLED ACCESS01 / READ THE INBOUND OBJECTFAX · REFERRAL · AUTH · SECURE MESSAGE02 / STRUCTURED FIELD CONTRACT03 / RISK + CONFIDENCE GATEIMMUTABLE TRACE / SOURCE · EXTRACTION · ROUTE · REVIEW
Extract constrained fields from irregular documentsRoute by completeness, confidence, and riskKeep clinician authority and a trace of each decision
Multi-hospital healthcare networkHealthcare, clinical operations

From unstructured chaos to governed LLM workflows in healthcare intake

A private, governed LLM workflow for intake classification and extraction improved queue speed, data quality, and clinical operations visibility without sacrificing controls.

LLM SystemsHealthcareRegulated Environment
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EMBEDDED LEADERSHIP / AI OPERATING MODELOne account connects the company’s workIllustrative model / anonymized engagement
Account-centered operating ontology for an AI-assisted small businessCRM opportunities, delivery projects, support tickets, and finance invoices relate to a shared account entity. AI assists proposal drafting, account-history summaries, next-action recommendations, and support triage. Functional owners retain decision rights. A weekly performance cadence connects pipeline, delivery, and support evidence to priorities and governance.Account-centered AI operating mapOpportunity, project, ticket, and invoice records connect to an account. AI assists proposals and support triage, while a human owner and weekly review govern decisions.CONNECTED ACCOUNT CONTEXTAI ASSISTS · PEOPLE OWN THE OPERATING SYSTEMDetailed account ontology and governed AI operating loopA human functional owner sets decision rights for a shared account context. The account links by typed relationships to an opportunity, a delivery project, a support ticket, and an invoice. AI assists proposal drafting and ticket triage. Integrated pipeline, delivery, and support evidence enters a weekly operating review that feeds the next priorities.ACCOUNT-CENTERED OPERATING MODEL
Shared context: sales, delivery, service, and billingAI assists existing workflows; owners approve commitmentsWeekly evidence informs the next investment and handoff
B2B services SMBProfessional services

Transforming a small business into a fully AI-driven company

Fractional technology leadership aligned priorities and ownership while AI assistants, automation, and integrated reporting turned manual bottlenecks into repeatable operations.

AI TransformationEmbedded LeadershipSmall Business
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DATA ARCHITECTURE / RETRIEVAL AIConnected information. Relevant context. Human judgment.Illustrative system model / anonymized engagement
Integrated data and semantic retrieval for technical supportIllustrative, anonymized system model. Multiple generic source systems integrate into a data warehouse. Knowledge content is prepared for a vector database. A technical question enters the retrieval path; the vector database returns relevant context, which is considered alongside integrated warehouse context by a support specialist. The diagram does not depict client data, specific products, or measured performance.01 / INTEGRATE02 / MODEL03 / INDEX04 / USESOURCE SYSTEMSSOURCE 01SOURCE 02SOURCE NDATA WAREHOUSEIntegrateModelServeSHARED DATA CONTEXTKNOWLEDGE CONTENTPREPARE + EMBEDVECTOR DATABASESCHEMATIC VECTOR SPACE?TECHNICAL QUESTIONRELEVANT CONTEXTDATA + RETRIEVALSUPPORT SPECIALISTHuman resolutionWarehouse and vector retrieval for technical supportIllustrative, anonymized system model. Multiple generic source systems integrate into a data warehouse. Knowledge content is prepared for a vector database. A technical question enters the retrieval path; the vector database returns relevant context, which is considered alongside integrated warehouse context by a support specialist. The diagram does not depict client data, specific products, or measured performance.SOURCESWAREHOUSEVECTOR DBCONTEXTSOURCE 01SOURCE 02SOURCE NKNOWLEDGEINGESTMODELSERVETO SUPPORTQUESTION / RETRIEVAL
Enterprise support organizationMulti-site services

A data and retrieval foundation for complex technical support

A connected warehouse and vector-backed retrieval layer bring information from multiple systems into technical-support investigations.

Data ArchitectureSystem IntegrationRetrieval AI
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