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Solutions

What we can build, with the architecture shown.

These solution concepts show how we approach common business problems with software and AI. Each one is an illustrative example, clearly labeled. None describes a specific client, and none reports results.

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About these examples. Concepts describe the kind of system we design and build, how we would approach it, and what we would measure. We publish client work only with the client’s permission, and verified case studies will be labeled as such.

Solution concepts

Illustrative conceptGenerative AI

Knowledge assistant with retrieval

An internal assistant that answers questions from approved documents, cites its sources, and hands off to a person when it is unsure.

Employee Web or chat Assistant API Auth · routing Language model Grounded answer Evaluation Tests · feedback Expert handoff Low confidence Retrieval Permission-aware Search index Chunks · embeddings Ingestion Parse · chunk Sources Docs · tickets

The business problem

Support and operations teams search wikis, PDFs, past tickets, and policy documents to answer routine questions. Answers vary depending on who is asked, and new staff take longer to become effective.

How we would approach it

Index approved sources with permission metadata, retrieve relevant passages for each question, and generate an answer with citations. Low-confidence answers route to a subject-matter expert, and every answer can be rated.

Key components

  • Document ingestion and chunking
  • Permission-aware retrieval
  • Answers with source citations
  • Evaluation set from historical questions
  • Feedback capture and escalation

What we would measure

  • Accuracy on an agreed evaluation set
  • Citation correctness
  • Escalation rate
  • Time to resolve routine questions

Baselines and targets are agreed during discovery. This concept does not describe a past engagement or reported result.

Related: AI and Machine Learning, Custom Software Engineering

Illustrative conceptAI for operations

Document intake and extraction pipeline

A pipeline that reads incoming documents, extracts structured fields, validates them, and sends only exceptions to a person.

Intake Email · upload · API Classification Document type Extraction Fields · tables Validation Business rules Review queue Exceptions only Audit trail Every decision System of record ERP · CRM

The business problem

Invoices, applications, or contracts arrive as PDFs and email attachments. Staff re-key fields into business systems and chase missing information by email.

How we would approach it

Classify each document, extract fields and tables, and validate results against business rules and reference data. Clean records post to the system of record; exceptions go to a review queue with the source document alongside.

Key components

  • Intake from email, upload, and API
  • Classification and field extraction
  • Rule-based validation
  • Review queue for exceptions
  • ERP or CRM integration
  • Audit trail for every decision

What we would measure

  • Field-level extraction accuracy
  • Share of documents needing no correction
  • Exception turnaround time
  • Errors found after posting

Baselines and targets are agreed during discovery. This concept does not describe a past engagement or reported result.

Related: AI and Machine Learning, Enterprise Modernization

Illustrative conceptEnterprise modernization

Incremental legacy modernization

Replace a business-critical legacy application one capability at a time, without a risky all-at-once rewrite.

Users and clients Unchanged experience Routing layer Stable interface Legacy application Shrinking scope Legacy database Read during transition Parity tests Old vs. new New services Tested · documented New data store Owned per service

The business problem

A core application built years ago is slow to change, hard to staff, and tightly coupled to its database. A full rewrite would take too long and put operations at risk.

How we would approach it

Place a routing layer in front of the legacy system, then move capabilities one at a time into new services with their own tests and data. Old and new run in parallel until parity tests show each step is safe to complete.

Key components

  • Routing layer with a stable interface
  • Capability-by-capability extraction
  • Data synchronization during transition
  • Parity tests against legacy behavior
  • Decommissioning plan

What we would measure

  • Share of traffic served by new components
  • Lead time for changes
  • Incidents during cutover
  • Legacy components retired

Baselines and targets are agreed during discovery. This concept does not describe a past engagement or reported result.

Related: Enterprise Modernization, Cloud and Data Engineering

Illustrative conceptCustom software

Multi-tenant SaaS platform foundation

A production platform foundation with tenant isolation, roles, billing integration, and delivery automation.

Web app Customers Mobile app Customers Platform API Versioned Identity and tenancy Roles · isolation Core services Domain logic Observability Logs · alerts Billing provider Subscriptions Tenant-scoped data Isolated access Admin console Support · ops

The business problem

A startup has validated demand with a prototype and now needs a platform that can securely serve many customer accounts and support a growing product team.

How we would approach it

Build tenant-aware data access, role-based permissions, subscription billing integration, an admin console, audit logging, and CI/CD, so the product team can focus on features rather than foundations.

Key components

  • Accounts, roles, and tenant isolation
  • Billing provider integration
  • Core domain services and API
  • Admin console for support and operations
  • CI/CD with automated tests
  • Logging, metrics, and alerting

What we would measure

  • Deployment frequency
  • Time to onboard a new customer account
  • Error rates and latency against agreed targets

Baselines and targets are agreed during discovery. This concept does not describe a past engagement or reported result.

Related: Custom Software Engineering, Cloud and Data Engineering

Illustrative conceptCloud and data engineering

Operations data platform

Automated pipelines from business systems into a governed warehouse, with shared metric definitions everyone can trust.

CRM Pipeline · accounts Billing Invoices · plans Support desk Tickets Ingestion Incremental loads Quality checks Tests · freshness Cloud warehouse Raw → modeled Dashboards Teams · leadership Shared metrics Defined once AI and ML Future use cases

The business problem

Revenue, operations, and support data live in separate SaaS tools. Reports are assembled by hand each week, and numbers rarely match between teams.

How we would approach it

Ingest data automatically from source systems into a cloud warehouse, transform it with tested models, define shared metrics once, and expose governed datasets for dashboards and future AI use cases.

Key components

  • Source system connectors
  • Raw and modeled data layers
  • Data quality and freshness tests
  • Shared metric definitions
  • Dashboards and access controls

What we would measure

  • Report preparation time
  • Data freshness
  • Quality issues caught before reaching dashboards
  • Agreement on metric definitions

Baselines and targets are agreed during discovery. This concept does not describe a past engagement or reported result.

Related: Cloud and Data Engineering

Illustrative conceptAI agents

AI agent for request triage

An agent that categorizes, enriches, and routes incoming requests, with human approval for higher-risk actions.

Knowledge base Read-only Account lookup Scoped API Auto-route Low-risk actions Shared inbox Incoming requests Agent Plan · act · log Policy check Risk thresholds Ticketing system Assigned queue Action log Audit · evaluation Human approval Higher-risk actions

The business problem

A shared inbox receives requests that must be categorized, matched to an account, prioritized, and routed to the right team. Manual triage takes hours each day and quality varies.

How we would approach it

An agent reads each request, looks up account context through scoped, read-only APIs, and proposes a category, priority, and draft response. Policy rules decide which actions run automatically and which wait for a person to approve.

Key components

  • Inbox and ticketing integration
  • Scoped tool access through APIs
  • Policy and approval rules
  • Draft responses for review
  • Complete action log
  • Evaluation against historical requests

What we would measure

  • Routing accuracy
  • Time from receipt to assignment
  • Approval override rate
  • Agent action error rate

Baselines and targets are agreed during discovery. This concept does not describe a past engagement or reported result.

Related: AI and Machine Learning, Enterprise Modernization

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