Developing and Deploying Operational Intelligence for Humans and AI Agents Alike
Meeting preparation cut from 15 minutes to 2. Timesheets 75% faster. Live dashboards replacing weekly spreadsheets. Governed AI agents in production. All built on a unified data layer connecting five operational systems.
Firm type
Specialist PE advisory consultancy
Team size
20+ staff
Source systems
5 (CRM, resource planning, finance, HR, documents)
Deployment model
Managed service
Status
Integrated MI, natural language querying, and live, governed AI agents in operation
The Challenge
The firm advises private equity houses and their portfolio companies on AI strategy, governance, and implementation. Like most specialist consultancies, their operational data was spread across five disconnected systems: a CRM for pipeline and client relationships, a resource planning tool for project delivery and timesheets, a finance system for invoicing and revenue, an HR platform for team management, and a document store for proposals, deliverables, and internal documents.
Each system worked well in isolation. The problems emerged at the boundaries. Preparing for a client meeting meant checking the CRM for deal status, the project tool for delivery progress, the finance system for outstanding invoices, and the document store for recent documents. Answering ‘what do we know about this client?’ required a partner to synthesise information from five places, mostly from memory.
Institutional knowledge was particularly vulnerable. Pricing decisions, proposal strategies, and client relationship context lived in the heads of the people who had done the work. When team members moved between projects or left the firm, that context went with them. Meeting notes sat in personal folders. Engagement learnings were not systematically captured or linked to the client or project they related to.
Measurable Impact Across the Business
15 minutes → 2 minutes
Meeting Preparation
Preparing for a client meeting previously meant checking the CRM for deal status, the project tool for delivery progress, the finance system for invoices, and the document store for recent files. Now, a single question assembles the complete client picture: deals, engagements, team history, invoices, documents, and meeting notes. Context that took 15 to 20 minutes to gather is available in under 3.
75% faster
Timesheet Completion
Logging a full day’s time entries previously took over 10 minutes of switching between calendar, project plans, and the time tracking tool. The platform compares calendar entries against project allocations, identifies gaps, and drafts time entries for review. A day’s timesheets now take 2 to 3 minutes to review and approve.
Past work found in seconds, not hours
Finding Precedent for Proposals
Preparing a proposal previously meant asking colleagues ‘have we done something like this before?’ and hoping the right person remembered. The platform searches semantically across all indexed proposals, deliverables, and engagement documentation. Searching by meaning, not just keywords: ‘data strategy’ surfaces work filed under ‘analytics roadmap’ or ‘information architecture’ because the concepts are related. Relevant precedent is linked to its engagement context, so you can see what was proposed, what was delivered, and what the outcome was.
Weekly spreadsheet → live dashboard
KPI Visibility
Operational KPIs (pipeline health, utilisation, revenue, aged debt) were previously assembled manually into a weekly spreadsheet, drawing from three systems. The platform provides a live dashboard that updates automatically. The same data is queryable in natural language for ad-hoc questions. Leadership has continuous visibility rather than a weekly snapshot that is out of date by the time it is reviewed.
Full context from day one
Onboarding
New team members previously relied on colleagues to brief them on client history, internal processes, and project context over their first few weeks. Now they can query the platform from their first day: ‘show me the history with this client’, ‘what is our approach to pricing fixed-fee engagements?’, ‘where do I find the proposal template?’. All policies, procedures, and how-to documentation are a question away, providing an always-available onboarding assistant.
Context-aware, not keyword-dependent
Knowledge Retrieval
Finding past work previously meant knowing the right filename or folder structure, or asking the person who created it. The platform indexes over 1,000 documents and 300 meeting transcripts with semantic search, and links every document to its client, engagement, and project context. A search for a client surfaces everything related to them: proposals, deliverables, meeting notes, and internal documents. A search by concept finds relevant work even when the exact terminology differs.
How It Works in Practice
The numbers describe what was built. These scenarios describe what it feels like to use.
Meeting Preparation
A partner preparing for a client meeting asks the platform: ‘show me everything related to Meridian Partners’. Within seconds, they see every deal (won, lost, and open), the team members who have worked with them, all engagements and their status, outstanding invoices, every document in the client folder, and notes from the last three meetings. Previously, assembling this picture meant checking five systems and took 15 to 20 minutes. Now it takes one question.
Context-Aware Document Search
A consultant working on a new proposal asks: ‘what did we propose the last time we worked with a financial services firm on data strategy?’ The platform searches semantically across all indexed proposals, links the results to the engagements they relate to, and surfaces the most relevant precedents. The search understands context, not just keywords. ‘Data strategy’ matches documents about ‘information architecture’ and ‘analytics roadmap’ because the underlying concepts are related.
Knowledge Captured as a Byproduct
Meeting transcripts are automatically captured and linked to the relevant client and engagement records. When a new team member joins a project, they can ask the platform for a summary of recent client discussions without anyone needing to brief them manually. Knowledge is captured as a natural byproduct of working, not as a separate documentation task that competes with billable work for attention.
The Journey
The Attercop Agentic Framework follows a four-phase model. Each phase delivers standalone value and builds the foundation for the next. This deployment has progressed through the first two phases and is now running governed agents in production.
Phase 1 built the data foundation. Five source systems are connected through automated sync pipelines. The canonical data layer resolves identities across all systems. The relationship graph links entities with 12 relationship types including investment chains and service associations. This runs continuously and has done so since the first month of deployment.
Phase 2 made that data queryable. The conversational interface provides access to 27+ tools spanning analytics, graph traversal, knowledge search, time entry, and operational settings. The firm uses this daily for client queries, pipeline reviews, and operational questions. It is embedded in how the team works.
Phase 3 introduced governed agents. Agentic capabilities are live in production, including an estimation agent that draws on historical engagement data to generate scoping models for new work. The governance framework constrains what agents can see and do, with full audit trails on every action. Additional agents are being deployed progressively as the framework matures.
Phase 4 is the next horizon: agents that have proven reliable over weeks of governed operation earn expanded permissions. The data foundation, natural language layer, and governance infrastructure are already in place. Earned autonomy is the natural next step, not a leap of faith.
Data Foundation
Five systems connected, identities resolved, sync pipelines operational
CompleteNatural Language
Semantic search, natural language querying, cross-system access
CompleteGoverned Agents
Agentic capabilities live in production, including estimation and operational workflows
Live – OngoingEarned Autonomy
Agents earn expanded permissions through demonstrated reliability
UpcomingData Foundation
Five systems connected, identities resolved, sync pipelines operational
CompleteNatural Language
Semantic search, natural language querying, cross-system access
CompleteGoverned Agents
Agentic capabilities live in production, including estimation and operational workflows
Live – OngoingEarned Autonomy
Agents earn expanded permissions through demonstrated reliability
UpcomingInterested in What This Could Look Like for Your Firm?
Every firm's systems and priorities are different, but the pattern is consistent: fragmented data, manual synthesis, knowledge that does not transfer. If that sounds familiar, we would welcome a conversation about how the Attercop Agentic Framework applies to your situation.