AI copilot for enterprise workflows.
Prometheas builds enterprise copilots that sit inside real workflows: service desks, Salesforce service operations, ServiceNow operations, claims and case teams, shared-services groups, and internal knowledge estates. The focus is not a generic chatbot; it is a governed assistant that improves daily work with source grounding, permissions, evaluation, and human review.
Teams find policies, ticket context, account history, or operational guidance faster without leaving the workflow.
Summaries, drafts, classifications, and recommendations follow governed sources and review paths rather than individual habits.
Quality, cost, usage, permissions, feedback, and escalation are visible enough for leaders to manage after launch.
Pressures shaping
the buying decision.
- Knowledge exists but work still slows down
Teams have policies, tickets, cases, documents, and system records, but staff still spend too much time searching, summarizing, and stitching context together.
- Generic chatbots do not fit enterprise workflows
A standalone assistant can answer questions, but service teams need context, permissions, action boundaries, escalation, and audit history.
- Sensitive workflows need control
Regulated or customer-facing teams cannot let a model invent answers, expose restricted data, or take actions without review.
- No quality standard for AI responses
Leaders need evaluation sets, feedback loops, answer traceability, and production monitoring before a copilot can be trusted.
The plays we run
to ship safely.
- Workflow-first copilot design
We map the user journey, source systems, decisions, handoffs, risk levels, and human approval points before choosing the model or interface.
- Grounded retrieval and permissions
Answers are grounded in approved knowledge and operational data, with role-aware access and clear citations or source references where needed.
- Human review for sensitive actions
The copilot can summarize, draft, classify, recommend, and prepare next steps while humans approve high-risk or customer-visible actions.
- Evaluation before scale
We build scenario tests, expected-answer sets, red-team prompts, feedback capture, and quality dashboards before expanding usage.
Where this
creates leverage.
- Service desk copilots
Summarize incidents, suggest next actions, retrieve known fixes, prepare handoffs, and route escalations inside ServiceNow or similar tools.
- Salesforce service copilots
Surface customer context, draft responses, summarize cases, recommend knowledge, and prepare follow-up tasks for service teams.
- Claims and case copilots
Read evidence, identify missing information, summarize documents, draft internal notes, and escalate decisions with an audit trail.
- Internal knowledge copilots
Answer employee or shared-services questions from governed policy, process, product, and operational knowledge sources.
Sized to the
risk and scope.
- Copilot opportunity assessment
2-3 weeks to map workflows, users, source systems, risk tiers, permissions, and the best first copilot use case.
- Governed copilot pilot
8-12 weeks for one workflow with retrieval, permissions, evaluation, human review, and adoption instrumentation.
- Production copilot rollout
12-20 weeks for a broader release with integrations, monitoring, operating routines, feedback loops, and support handoff.
- Managed copilot improvement
Monthly quality review, prompt and retrieval tuning, cost control, model evaluation, and new workflow expansion.
Supporting systems, not the main story.
We choose tools around ownership, risk, integration needs, and lifecycle cost. Buyers should see this as implementation support, not the definition of the solution.
What the engagement includes.
- Enterprise copilot discovery and workflow mapping
- RAG architecture and governed knowledge retrieval
- ServiceNow, Salesforce, portal, and internal-tool embedding
- Role-based permissions and source-level access control
- Prompt, model, and answer-quality evaluation
- Human review queues, escalation paths, and audit logs
- Production monitoring, cost controls, and managed improvement
Enterprise IT services and shared services
AI-assisted knowledge and service-triage pattern for teams with large internal knowledge estates.
Grounded knowledge retrieval
Financial-services operations
Salesforce service operations modernization for regulated customer and internal service teams.
Case ownership and escalation redesign
Enterprise technology / SaaS operations
CMDB and ITOM modernization for operations teams that needed service-impact clarity.
Trusted source ownership for priority CI classes
“A product is not finished when it launches. It is finished when customers can use it and the business can run it.”
Leads digital product engineering for customer portals, SaaS platforms, mobile apps, cloud modernization, and AI-enabled product experiences.
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