Workflow is undefined
Nobody has decided whose work, which job, and how far it should change.
METHOD · FORWARD-DEPLOYED AI ENGINEERING
We don't stop at a proposal.
We go on site and leave a working system.
Strassen engineers work inside the customer's environment to understand the real workflow, data, authority, and success criteria. From there we design AI workers, MCP, Agent Skills, evaluation, approval, and audit as one system, and we own production deployment and outcome verification — not a PoC.
Forward-Deployed AI Engineering is Strassen's delivery model: we enter the customer's place of work, understand the workflow, data, authority, and success metrics, design AI workers, tools, MCP, skills, evaluation, approval, and audit as one system, and implement until the production outcome is verified.
WHYMove from using AI to operating an AI workforce.
WHATAI Workforce Infrastructure
HOWForward-Deployed AI Engineering — how we enter the customer's field, what we discover, how we implement, and how we close on outcomes
CONTROLhiyu — AI Workforce Control Plane — how AI workers are selected, assigned, connected, executed safely, and proven complete
PROOFMake AI accountable for real work.
THE LAST MILE
The model is not what's missing.
The last mile to real work is.
Data has no operating contract
The data's location is known, but not its freshness, permissions, ownership, or update path.
Actions are unsafe
Search works, but updates, sending, approvals, and execution cannot be delegated safely.
Success is not measurable
The demo looks good, but time, quality, cost, and completion rate are not measured.
No production owner exists
Nobody owns failures, exceptions, model updates, or the state after a restart.
Strassen implements this last mile as one system: workflow, data, AI workers, authority, evaluation, and operations.
STRASSEN FORWARD LOOP
From Discover to Compound.
One team, end to end, then the next workflow.
We own all six stages — Discover → Ground → Build → Verify → Deploy → Compound. The last one, Compound, is what separates a product company from a contractor.
Discover — find the work
Who does the job, which event starts it, inputs and deliverables, waiting and rework, decisions that carry responsibility, current quality, time, and cost, and what counts as success.
- Workflow map
- Problem statement
- Baseline metrics / success criteria
- Risk / approval map
Ground — connect data and authority
Source of truth, freshness, sensitivity, identity-to-permission mapping, API / database / document / SaaS connections, the boundary between retrieval and write actions, audit requirements.
- AI-ready data map
- Context contract
- Tool / MCP inventory
- Authorization model / data handling policy
Build — agents, tools, skills
AI worker roles, prompt / context policy, MCP servers, tool contracts, Agent Skills, subagent structure, structured output, deterministic validators, human approval UI, failure recovery.
- Working AI workers and tool contracts
- Approval UI and recovery paths
Verify — evaluation and safety
Task completion rate, correctness, unsupported action rate, human escalation rate, permission violation block rate, regression, latency, cost per completed task, failure recovery, restart durability, result persistence / read-back.
- Eval suite and regression tests
- Safety boundary verification record
Deploy — into production
Exact candidate version, execution through the production route, verification from the external route, monitoring, logs, traces, alerts, rollback procedure, operator runbook, owner and escalation route, post-restart state check.
- Release receipt
- Runbook / rollback
Compound — turn learning into assets
Agent Skills, tool templates, MCP connectors, eval suites, security patterns, deployment checklists, starter repositories, playbooks, and hiyu product requirements.
- Reusable platform assets
- Requirements fed back into hiyu
WHAT WE IMPLEMENT
Not an abstraction.
Units you can actually start.
FDE × HIYU
Discover in the field.
Govern with hiyu.
Forward-Deployed AI Engineers discover the customer's specific work and constraints. hiyu turns those requirements into AI worker identity, placement, context, authority, approval, execution, audit, and proof of completion — operated as a continuously working AI workforce, not a one-time demo.
| Customer challenge | What FDE defines | What hiyu controls |
|---|---|---|
| Which AI is right is unclear | role / quality requirement | worker selection / assignment |
| Data is scattered | context / source contract | context delivery / access route |
| Uncontrolled execution is scary | action boundary | policy / approval / audit |
| Completion can't be trusted | success / evidence contract | completion / persistence / read-back |
| Workers are unstable | liveness / recovery rule | runtime route / health / restart |
| Every project starts from scratch | reusable pattern | skill / connector / template reuse |
HOW WE MEASURE
Not "we adopted AI."
How the work changed.
Business Outcome
- Cycle time
- Hours saved
- Cost per case
- Revenue impact
- Error / rework reduction
- Customer response time
Task Quality
- Completion rate
- Accuracy / task fidelity
- Human escalation rate
- Rejection / correction rate
- Policy violation block rate
Production Reliability
- Availability
- Retry success
- Mean time to recovery
- Result persistence
- Restart durability
- Audit completeness
Adoption
- Active users
- Repeat usage
- Eligible workflows handled
- User override rate
- Time to competent use
EVIDENCE STATUSThese are the metrics Strassen measures as standard. This page does not yet publish verified results. We will publish results as they are confirmed, each with customer permission, baseline, measurement window, and verification date.
ENGAGEMENT
Start with one workflow.
Expand on outcomes verified in production.
First, pick one workflow that is stuck. In Discover we agree on success metrics and approval points, build something that runs in the real environment, verify the outcome through the production route, and only then widen the scope. Duration, team, and pricing are proposed individually once we understand the data boundary and acceptance criteria.
First Verified Workflow
Close one workflow from Discover to production read-back. Deliverables: workflow map, work contract, least-privilege policy, completion receipt, runbook.
Expand
Extend to adjacent workflows within the same boundary. Reused skills, connectors, and evals make the second deployment faster.
Operate with hiyu
Operate AI worker selection, placement, authority, and proof of completion on hiyu, and hand over to your own team.
CASE STUDYA publishable case study is in preparation. We will publish once customer permission and production read-back are in place.
FAQ
Frequently asked questions
How is this different from contract development or AI consulting?
We own discovery, implementation, production deployment, outcome verification, and feedback into the product as one continuous responsibility. We don't stop at advice or a specification: we leave something that runs inside the customer's constraints and return it to hiyu as reusable skills, connectors, and evals.
Can we proceed without sending internal data outside the company?
Yes. Self-hosted, dedicated cloud, or local LLM configurations keep data inside your boundary, chosen to fit your requirements. In the Ground stage we define the source of truth, sensitivity classes, and the boundary between retrieval and write actions as a contract.
What stops the AI from acting on its own?
We design the action boundary, the operations that require human approval, and the audit log first. hiyu checks policy, approval, and liveness right up to the moment of execution and blocks permission violations.
What counts as "complete"?
Not "the AI replied." The intended effect occurred in the real system, the result was persisted, and the state can be read back after a restart. We never mistake a passing test, a PR, a merge, or a deploy for completion.
Which workflow should we start with?
One where ROI, failure cost, data boundary, and acceptance criteria can be defined clearly. In the first conversation, tell us the candidate workflow, your current tools, the data boundary, and your timing.
BRING ONE WORKFLOW
Bring us one workflow
that refuses to leave pilot mode.
We will connect the model, company context, authority, execution, verification, and ongoing operation inside the real environment.