Multi-workflow automation architecture
Full system design covering all automation workstreams — agent responsibilities, integration points, shared infrastructure, and governance model.
Integrated Systems Platform
Multiple workflows automated. One AI-connected intelligence layer. The whole team benefits.
- Investment
- $60,000 – $120,000
- Timeline
- 5–8 months
Overview
What this engagement is.
This is for organizations that are done with incremental improvements and want to transform how they operate. Not one automation — a platform. Multiple workflows running autonomously, a shared AI intelligence layer that connects your data across systems, and assistants deployed across your teams. It is an engineering project with a defined scope, milestones and a fixed price — not a research project.
An Integrated Systems Platform engagement spans 5–8 months and covers everything from architecture to launch: multiple automated workflows, a centralized data and AI layer, cross-team assistants, and the documentation and training your team needs to extend it themselves. We work in parallel workstreams so you see results throughout the engagement, not just at the end.
What you can rely on
- Fixed price — you know the total cost before we start
- Parallel workstreams mean you see results throughout, not just at month 5
- Platform is designed to be extended by your own team after handoff
- 30-day post-launch support is included, not an add-on
Deliverables
What you receive.
Centralized AI intelligence layer
A shared data layer that connects your systems and gives every AI component access to the same grounded, real-time business context.
Automated workflows — built and deployed
Each workflow built, integrated, tested, and deployed independently. Parallel workstreams mean earlier delivery.
AI assistants across business units
Team-specific assistants deployed into the tools each group uses, tuned to their actual workflows and data.
Monitoring, alerting, and audit infrastructure
Full observability from day one. Every automated decision is logged, anomalies are alerted, and dashboards make system health visible.
Documentation, training, and 30-day post-launch support
Your team learns to operate and extend the platform. We stay engaged for 30 days after launch to resolve issues and tune performance.
Process
What happens from here.
Discovery and architecture
Weeks 1–4
We map all target workflows, design the full system architecture, and produce a build plan you review and approve before we start. Stakeholder alignment and sign-off is part of this phase.
Infrastructure and data layer setup
Weeks 5–6
Shared AI infrastructure, data integrations, security reviews, and baseline observability are laid down first — everything else builds on this.
Parallel workflow builds
Weeks 7–20
Multiple workstreams run simultaneously. You receive weekly updates and milestone demos throughout.
Integration, QA, and load testing
Weeks 21–28
All components are integrated and stress-tested against production-scale data volumes. User acceptance testing with real stakeholders before launch.
Training, documentation, and launch
Weeks 29–32
Team training, written documentation, and a staged launch. We stay on for 30 days to monitor and resolve.
Related experience
Where this approach has been applied.
Representative engagements led by our principal engineer. Client names withheld.
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Get started
Tell us about the transformation
Give us the big picture. We'll scope from there and come back with a realistic architecture proposal.
- Reply within 1 business day. A direct response from a person, not an auto-responder.
- Free introductory call. A technical conversation to understand the actual problem. No sales pitch.
- Honest assessment. If this engagement is not the right fit, we will say so and suggest what would help.
- Written proposal. If we are aligned, you receive a plain-language scope, timeline and fixed price.