Step 1
Client profile setup
Define core client attributes: industry, size, communication preferences, compliance frameworks, and key stakeholders.
Output that is client-ready, not client-adjacent
Client context captures each client's communication preferences, compliance requirements, strategic priorities, and relationship history. Agents produce work tailored to the client, not just the task.
AI that doesn't know your clients produces generic work
Every client has preferences that take months to learn: how they like reports structured, which topics are sensitive, what compliance frameworks apply, how formal their communications should be.
Without client context, AI agents treat every client the same. The output technically answers the brief but feels generic — missing the nuance that separates a draft from a deliverable.
Teams compensate by adding long client-specific instructions to every prompt, which is error-prone, inconsistent across team members, and impossible to maintain as the client relationship evolves.
What client context captures
Client context is the relationship layer. It encodes everything your team has learned about working with a specific client over the life of the relationship.
This includes communication style and formality preferences, industry-specific compliance and regulatory requirements, strategic priorities and current focus areas, stakeholder map with decision-makers and influencers, sensitive topics and known constraints, and historical preferences learned from past deliverables and feedback.
Client context accumulates over time. The more you work with a client, the more precise agent output becomes for that relationship.
How client context gets built
Step 2
Relationship import
Pull in CRM data, past deliverables, and email patterns to bootstrap the client context with existing relationship intelligence.
Step 3
Interaction learning
As your team delivers work, Sempleo observes what gets approved, what gets revised, and infers client preferences.
Step 4
Cross-team sharing
Client context is shared across teams working with the same client. Account managers, consultants, and support all benefit from the same knowledge.
What lives in client context
Communication preferences
Formality level, preferred document structure, executive summary expectations, and whether the client prefers detail or brevity.
Compliance requirements
Industry regulations, data handling rules, required disclaimers, and approval workflows specific to this client's regulatory environment.
Strategic priorities
Current business objectives, transformation initiatives, and focus areas that agents reference when framing recommendations.
Stakeholder map
Key decision-makers, their roles, communication styles, and known preferences — so agents tailor output to the audience.
Relationship history
Past engagement summaries, feedback patterns, and lessons learned that prevent agents from repeating known mistakes.
Sensitive topics
Areas to avoid, politically charged subjects, and constraints that agents must respect in all client-facing output.
How agents use client context
When an agent runs with a client scope, it combines company voice, team methodology, and client-specific knowledge. A proposal to Client A reads differently from a proposal to Client B — even when both use the same team methodology — because the framing, formality, and emphasis adapt to each relationship.
This is the layer that eliminates the "generic draft → heavy editing" cycle. Output arrives already shaped for the client's expectations, compliance needs, and communication style.
For account teams managing multiple clients, switching between client contexts is instant. The agent carries the full relationship history so you don't have to re-brief it every time.
See client context in action
See how client knowledge improves every deliverable
Book a demo and we will show you client-aware agents working with real relationship data.