CRM-grounded sales assistant
Inbox replies, call notes, and CRM context fused into a single workspace.
AI Integration
We turn AI from a talking point into infrastructure.
Kaizenhaus integrates AI into the workflows that actually run your business — with humans in the loop, secure connections, and the production telemetry to know it is working.
Where it starts
AI readiness
Before integrating anything, we assess where the business is. Data quality, integration surface, security posture, operator capacity, and the workflows that would benefit most from AI involvement.
- Workflow inventory and friction map
- Data and integration audit
- Security and compliance baseline
- Operator readiness review
The architecture
Workflow mapping
Every integration begins with a precise map of the workflow as it actually runs — owners, exceptions, edge cases, the silent escalation paths nobody documents.
- Owner and decision-point mapping
- Exception and escalation paths
- System-of-record clarity
- Quantified time and cost analysis
The substrate
Data and knowledge systems
AI is only as good as what it can see. We design embeddings pipelines, retrieval architectures, and grounded knowledge bases that hold up under real-world use.
- Embeddings and vector store design
- Hybrid search (keyword + semantic)
- Citations, grounding, and provenance
- Freshness and re-indexing strategy
The actors
AI agents
Agents take action. We design agents with clear jobs, scoped tools, durable memory, and explicit handoffs to humans when confidence drops or stakes rise.
- Tool use and structured outputs
- Planning, retries, and timeouts
- Memory and retrieval-augmented context
- Evaluation and regression harnesses
The trust layer
Human-approval flows
Approval flows fail when they slow operators down. We design approval queues that are fast, scoped, and embedded in the tools your team already uses — not buried in another dashboard.
- Inline approval in existing tools
- Confidence thresholds and routing
- One-click acceptance and override
- Audit log and reviewer accountability
The boundaries
Security and governance
AI integrations touch sensitive systems. We design with vault-backed credentials, scoped tokens, audit logging, and clear data-residency boundaries from day one.
- Vault-backed credential management
- Least-privilege scoped tokens
- Full audit logging and traceability
- Data residency and PII handling
Integration examplesExample Example Example Example Example Example
Six places AI earns its keep.
Support agent copilot
Suggested replies grounded in your docs, tickets, and product changelog.
Document intake automation
Extract, classify, validate, route — with human review where it matters.
Pricing and quoting agent
Generates quotes from your pricing rules and routes for human approval.
Operations exception inbox
AI-prioritised exceptions, ranked by business impact and resolution effort.
Knowledge concierge
A grounded answer engine for the questions employees ask each other in Slack.
Implementation roadmap
From workshop to production in eight weeks.
A typical AI Build Sprint shape. Real timelines depend on integration complexity and data quality, but the rhythm is consistent.
Weeks 1 — 2
Discovery and mapping
Workshops with operators and leadership. Workflow map, data audit, integration inventory.
Weeks 3 — 4
Architecture and prototype
Target architecture, security model, and a working prototype on real data.
Weeks 5 — 7
Production build
Hardening, integrations, observability, and human-in-the-loop UX.
Week 8+
Rollout and iterate
Quiet rollout, operator training, telemetry review, tuning loop.