01
Opportunity and pilot decisions
Compare business value, feasibility, data readiness, dependencies, risk, and ownership. The output is a build, buy, rescue, or stop recommendation, plus a bounded delivery plan when the work should continue.
Enterprise AI services
Move enterprise AI from scattered pilots to production work.
If you lead AI work across multiple systems and owners, we help you choose the right opportunity, build or rescue the system, connect it to existing software and data, and define who runs it after launch.
Bring a selected workflow, a stalled pilot, or an AI portfolio that needs a first decision.
Context
Where we help
AI work gets harder when the workflow crosses systems and owners.
A useful AI idea can touch several applications, data owners, approval paths, and operating teams. That is where many pilots lose momentum.
We narrow the work to one owned workflow, make the success test explicit, then recommend whether to build, buy, rescue, or stop.
01
Capabilities
A practical path through enterprise AI delivery.
Use one service or carry the same workflow from the investment decision through production operation.
02
Workflow systems and integration
Build the smallest AI system that can meet the operating requirement, then connect it to the software and data the workflow already uses. The right design may be an embedded feature, an assistant, an agent, or conventional automation with AI at one step.
03
Production operation and transfer
Add evaluations, traces, alerts, fallback behavior, and a runbook before launch. Your team can take over the system, or MavenSolutions can operate it under a separate managed service.
02
Representative workflows
Examples of how the work can be framed.
These are solution patterns, not client case studies. Scope, controls, and measures are set against the client's workflow and data.
Permission-aware knowledge access
An internal assistant retrieves from approved policies, procedures, and project material. It cites the source behind each answer, respects existing permissions, and declines when the evidence is weak.
Possible measures
- Answer accuracy
- Unsupported answer rate
- Time to find an answer
- Use by the intended team
Document and exception processing
A production AI system extracts required fields, checks them against systems of record, and routes discrepancies to the right person with the exception identified.
Possible measures
- Field accuracy
- Exception rate
- Cycle time
- Rework
- Cost per document
Pilot production rescue
A promising pilot cannot pass security review, integrate with the workflow, or perform reliably outside a demo. We inspect the code, data, evaluations, integrations, and operating model, then recommend repair, rebuild, or stop.
Possible measures
- Acceptance score
- Failure rate
- Latency
- Cost per run
- Recovery from known exceptions
03
Production controls
The operating model is part of the build.
Before production, we document permitted sources and actions, identity and access rules, human approval points, logging requirements, fallback behavior, and the person responsible for the workflow.
The engagement produces evidence the client can inspect: a signed workflow specification, a runnable evaluation harness, integration configuration, production monitoring, and a runbook.
- 01Access
- Existing identity and source permissions carry into the AI workflow.
- 02Review
- Named people approve sensitive or uncertain actions.
- 03Recovery
- Fallback behavior and exception ownership are defined before launch.
- 04Evidence
- Evaluations, traces, and operating records stay inspectable.
Fit and next step
Put one enterprise AI decision on firmer ground.
Bring the workflow, pilot, or portfolio question that needs an accountable next step. We will identify the right service and what must be known before delivery begins.
Free45 minutesFit and next step