Solutions
Built around outcomes, not technologies.
Each solution targets a specific operational bottleneck, decision, or workflow, connected to the systems you already run, governed from the first production use case.
AI Operations Copilots
Triage, investigate, summarize, and route operational work so your team spends its time on decisions instead of digging.
The problem
- Exceptions surface days late, buried in reports nobody has time to read
- Every investigation starts from scratch across five systems and a spreadsheet
- Ownership is unclear, so issues bounce between teams
What we build
- Exception detection over orders, inventory, invoices, tickets, or deliveries
- Evidence-linked explanations of what changed and the likely drivers
- Routing to the accountable owner with an approval step before anything moves
Deliverables
- Detection rules and models tuned to your thresholds
- Copilot surface in Slack, Teams, or a web app
- Approval workflow with audit history
Example workflow
A distributor's copilot flags margin-eroding orders each morning, explains which pricing or freight change caused them, and drafts the correction for the account owner to approve.
Systems involved
Success metric
Time to identify and resolve an exception; manual reporting hours per week.
Security model
Copilots read only the records the requesting user may see. Actions require a named approver and are logged with the evidence shown.
Data Intelligence
Reconcile, detect anomalies, forecast, and generate executive reporting, with every number traceable to the source row.
The problem
- Finance, sales, and product each report a different version of the same metric
- Month-end reconciliation is a week of manual work
- Forecasts live in someone's head or a fragile spreadsheet
What we build
- Automated preprocessing, analysis, and visualization over warehouse, database, and file sources
- Anomaly detection and variance explanations for revenue, cost, and operations metrics
- Narrative executive reporting with drill-down to the underlying records
Deliverables
- Governed semantic layer for the metrics in scope
- Scheduled analyses with anomaly alerts
- Executive reporting with source lineage
Example workflow
A SaaS company's weekly revenue brief reconciles Salesforce, Stripe, and product usage, explains pipeline movement, and flags the three accounts whose behavior changed most.
Systems involved
Success metric
Forecast accuracy; hours to close; number of unexplained variances per period.
Security model
Metrics definitions, access rules, and lineage are versioned. Reports show which sources and filters produced each figure.
Knowledge and Support Agents
Secure internal search and policy-aware answers for employees and customers, grounded in the documents and records you approve.
The problem
- Answers depend on who you know, not on what is written down
- Support and sales teams re-derive the same answers daily
- Generic chat tools leak context or invent policy
What we build
- Permission-aware retrieval over Drive, SharePoint, Notion, Confluence, and ticket history
- Agents that cite the source and refuse when the answer is not in approved material
- Support-assist and sales-assist workflows that draft, never send, without review
Deliverables
- Indexed knowledge base with access control mirrored from source systems
- Agent surface in chat, help center, or web app
- Evaluation set and answer-quality dashboard
Example workflow
An internal agent answers onboarding, policy, and product questions with links to the governing document, and drafts Jira tickets when a process gap is found.
Systems involved
Success metric
Time to answer; ticket resolution time; onboarding time to productivity.
Security model
Retrieval respects source permissions per user. Every answer carries citations; unanswerable questions are logged rather than guessed.
Workflow Automation
Connect systems, draft the next action, route approvals, and track completion, so work moves without becoming ungoverned.
The problem
- Hand-offs between tools happen by email and memory
- Automation tools fire actions nobody can audit
- Approvals stall because the context is scattered
What we build
- Cross-system workflows via APIs, exports, warehouses, and custom connectors
- Drafted actions (updates, emails, tickets, journal entries) with a human approval gate
- Completion tracking and an auditable history of who approved what
Deliverables
- Integration layer with documented interfaces
- Approval routing with SLAs and escalation
- Run history, retries, and monitoring
Example workflow
When a customer downgrade hits Stripe, the workflow assembles account context from HubSpot and Zendesk, drafts the retention outreach, and routes it to the CSM for approval.
Systems involved
Success metric
Cycle time per workflow; work throughput; manual touches per case.
Security model
Agents act through scoped credentials. Impactful actions require explicit human approval and are reversible where the target system allows.
AI Platform Foundations
The data architecture, evaluation, observability, model routing, security, and governance that let the first use case become the tenth.
The problem
- Each AI experiment re-solves access, logging, and model choice from scratch
- No one can say how well the system performs or what it costs
- A single provider holds the prompts, the data, and the leverage
What we build
- Private AI data workspaces with structured context and scoped retrieval
- Model gateway with policy-based routing across providers and self-hosted models
- Evaluation harness, observability, audit logging, and cost controls
Deliverables
- Reference architecture and infrastructure as code
- Model gateway, eval harness, and observability stack
- Governance baseline aligned to NIST AI RMF functions
Example workflow
A scale-up replaces three disconnected AI pilots with one governed platform: shared retrieval, one audit log, and a routing policy that keeps sensitive data on a self-hosted model.
Systems involved
Success metric
Time to ship a new use case; evaluation pass rate; cost per task.
Security model
Designed for customer control, evidence, and portability: everything is exportable, every call is logged, and model choice is a configuration, not a rewrite.
Put AI to work without giving up control.
A fixed-scope Opportunity Sprint: your system map, three prioritized use cases, and a pilot plan in two weeks.