One Platform. Every System. Plain English.
The Attercop Agentic Framework connects the systems your firm runs on, resolves identities across them, and gives you a single interface to query everything in natural language. Then it deploys governed agents that act on what they find.
One View of Your Business. Three Ways to Use It.
Your firm's knowledge is scattered across CRM, resource planning, finance, HR, and documents. Each system holds a fragment. None of them know about the others. The Attercop Agentic Framework brings this together into a single connected knowledge layer: structured data unified through identity resolution, unstructured documents indexed with semantic search, and a relationship graph that maps how everything connects to everything else. Not a data warehouse. A knowledge graph that can be reasoned over.
See the Whole Picture
Dashboards that draw from every system simultaneously. Pipeline health from CRM, resource utilisation from project data, revenue and cash position from finance, team availability from HR. A single view that previously required a partner to check six systems and synthesise the answer in their head. Updated automatically. No manual assembly.
Ask Questions. Get Complete Answers.
A conversational interface over the entire knowledge layer. ‘What does our pipeline look like this quarter?’ draws from CRM, resource planning, and finance in a single answer. ‘Show me everything about Meridian Partners’ returns deals, engagements, team history, invoices, documents, and meeting notes. The platform knows which systems hold the answer. You do not need to.
Data Rich Enough for Agents to Reason Over
AI agents are only as good as the data they can access. An agent querying a single CRM has a narrow view. An agent with access to a connected knowledge graph that spans clients, engagements, team members, invoices, documents, and their relationships can reason across context, spot patterns, and surface insights that no single system could provide. The unified knowledge layer is what makes governed agents possible, not just technically, but usefully.
Built on Your Existing Systems
The knowledge layer is populated from the tools your firm already uses. CRM for pipeline and relationships. Resource planning for delivery and capacity. Finance for invoicing and revenue. HR for people and availability. Documents for proposals, deliverables, and meeting knowledge. Automated sync pipelines keep everything current. You do not migrate, replace, or change how your team uses these systems. The platform reads from them and connects what they cannot connect themselves.
One Client. One Truth. Regardless of Source.
Your CRM calls them 'Acme Ltd'. Your finance system calls them 'ACME Limited'. Your project files reference 'Acme'. Your HR system has team members allocated to 'Acme Corp'. These are the same client, but no system knows that.
The canonical data layer resolves these fragments into a single identity. Every client, engagement, team member, and document is linked to its authoritative record. When you ask the platform about a client, you get the complete picture: deals, engagements, team allocations, invoices, documents, and meeting notes. Not fragments from whichever system you happened to search.
The platform manages canonical entity types across three resolution tiers, from deterministic matching (exact identifiers) through to human review for ambiguous cases. Identity resolution is not a one-off migration. It runs continuously as new data enters the system.
Ask a Question. Get a Complete Answer.
“What is our pipeline looking like this quarter?”
Returns deal values, stages, and conversion rates drawn from CRM, overlaid with resource availability from the planning system and revenue forecasts from finance. One answer from three systems.
“Show me everything related to Meridian Partners.”
Returns all deals (won, lost, open), every engagement, team members who have worked with them, all invoices and payment status, every document in their folder, and meeting notes from the last twelve months. A complete client picture assembled in seconds.
“Who is available next week with financial modelling experience?”
Returns team members with relevant skills whose allocations show capacity, cross-referenced with HR data for absences. Answers the resourcing question without checking three systems manually.
“What did we propose the last time we worked with a similar firm?”
Returns semantically matched proposals and engagement documentation from past work, linked to the outcomes of those engagements. Institutional knowledge surfaced through context, not keyword search.
The natural language interface draws from structured data and unstructured documents simultaneously, spanning analytics, graph traversal, knowledge search, and operational tools. The same interface handles read queries and write operations (with human confirmation for mutations).
Agents That Work While You Sleep
Monday Morning Briefing
Before your first meeting of the day, the platform has assembled a briefing: client history, recent project updates, outstanding invoices, relevant documents, and notes from your last three conversations with this client. It checked your calendar, identified the meeting, queried five data sources, and presented the result. You did not ask for it.
Timesheet Nudge
At 5pm, the platform compares your calendar entries and project allocations against your logged time. It identifies a three-hour gap on a client project and drafts a time entry for your review. You tap approve, adjust, or dismiss. Over time, the agent learns your patterns and its drafts become more accurate.
Pipeline Health Monitor
A weekly digest arrives: two deals have not progressed in 30 days, one proposal is overdue for follow-up, and a delivery project is trending 15% over its time estimate. Each item links to the relevant records. At higher trust levels, the agent drafts follow-up emails and flags the resource overrun to the project lead.
Knowledge Preservation
A partner finishes a client call. The meeting transcript is automatically captured, linked to the client record, the active engagement, and the project in the planning system. Key discussion points are indexed for semantic search. Six months later, when a colleague asks 'what did we discuss with this client about pricing?', the answer exists. No one had to file anything.
Every agent operates at an explicit trust level: observe (read-only), suggest (propose actions for human approval), or act (execute within policy bounds). Agents begin at observe and earn higher trust through demonstrated accuracy over time. Every action is logged. Autonomy is revoked faster than it is granted.
A Platform That Gets Smarter Over Time
Most operational tools are static. They report on what happened. They do not learn from it.
The Attercop Agentic Framework captures decisions alongside the data that informed them. Every client interaction, every pricing decision, every resource allocation becomes part of a searchable institutional memory. Agents draw on this memory when making suggestions: 'the last time we staffed a project of this type, we allocated two analysts and a lead' or 'similar proposals in this sector were priced at this range'.
The longer the platform runs, the richer this context becomes. New team members can access institutional knowledge from their first day. Departing team members leave their context behind. The organisation's accumulated experience becomes an operational asset rather than a liability that walks out the door.
How It Fits Together
All three interfaces draw from the same unified data layer through the same governed service layer. The natural language interface, the agents, and the dashboards see the same data, filtered by the same access controls. There are no side doors.
See What It Looks Like for Your Firm
The specifics depend on your systems and your operational priorities. We are happy to walk through how the platform would connect to your environment and what it would look like in practice.