Business case: a human-AI teaming platform with real constraints
Executive summary
Commission Syntheos to build a human-AI teaming platform modeled on the Wicked Problems Lab at Georgetown. An AI orchestrator dispatches specialized agents and surfaces findings, but a delegation contract and database-level phase gates prevent the AI from verifying assumptions, defining success criteria, or advancing past a human judgment. Humans decide. The AI carries water.
The problem
Off-the-shelf AI tools are eager to give our users the answer. In a learning environment that's the wrong behavior. In a decision-critical workflow it's dangerous. We want the mechanical help — retrieval, synthesis, counterpoint, drafting — without the AI writing the part that a human is accountable for.
Proposed engagement
A 14 to 18 week engagement to deploy the teaming platform and tune it for our domain. We define the phase sequence, the success-gate criteria, and the agent library. Syntheos delivers the orchestrator, the agent tiers (fast, deep, and QA/QC), the delegation contract, the database-enforced phase gates, and an optional voice subsystem if we want voice interaction.
What we get
- A deployed teaming platform running on our infrastructure
- A library of specialized agents tuned to our use case
- The delegation contract that constrains AI authority
- PL/pgSQL phase gate stored procedures
- Instructor / operator tools for oversight and intervention
- Optional voice subsystem with transcript storage and embeddings
Risks and mitigations
The main risk is users wanting the AI to do more than the contract allows, and pushing back on the constraints. Mitigation: the contract is visible in the UI and explained in the onboarding, so users understand what the platform is for before they complain about what it refuses. A second risk is phase gate friction. Mitigation: the gate criteria are defined by our side, not Syntheos, so we control what it takes to advance.
Success metrics
- Users complete the phase sequence with meaningful AI assistance at each step
- The AI never advances a user past a gate without explicit human action
- Agent output gets surfaced when useful and suppressed when not
- Oversight staff can audit any session end to end
Investment and timeline
14 to 18 weeks for a first deployment. The deliverable is a platform we own, not a subscription we rent. Costs scale with the agent library and the voice subsystem. Specific pricing is fixed before kickoff.
Recommended next step
A walkthrough of the Georgetown Wicked Problems Lab, followed by a workshop with our team to define the phase sequence and agent library for our use case.