Turn critical workflows into production AI systems.
PlatformBridge embeds senior AI engineers with your team to map the workflow, integrate your systems, build and evaluate the agent, and move it into production — with measurable success criteria and a clean handoff.
Fixed scope · 2–4 weeks
· One workflow · You keep the code
The model is rarely the last mile.
Real deployments fail on workflow ambiguity, data access, integrations, evaluation, security and operational ownership. We own that last mile.
- 01
Our AI pilot isn’t production-ready.
- 02
Our enterprise customer requires integrations our product team doesn’t have time to build.
- 03
We have an agent demo, but we don’t trust it enough to let it operate.
- 04
Operations knows there is a workflow to automate, but nobody owns taking it end-to-end.
- 05
Every deployment is becoming custom.
None of these are model problems. All of them are engineering problems, and they are the reason a promising pilot sits at 80% for two quarters.
One workflow. One accountable team. A production outcome.
A fixed-scope engagement that takes one high-value workflow from current state to a tested production system — or an explicit production-readiness milestone.
- 01Workflow + exception map“Do you actually understand how our operation works?”
- 02Baseline and target KPI“What business result are we buying?”
- 03Architecture and control boundaries“What exactly are you building, and where does it run?”
- 04Working end-to-end vertical slice“Can this actually operate with our systems?”
- 05Eval dataset + acceptance threshold“How will we know whether the AI works?”
- 06Integration implementation“Can it access the tools and data needed to do the job?”
- 07Failure, fallback and escalation design“What happens when it is uncertain or wrong?”
- 08Production / readiness release“Can this leave the demo environment?”
- 09Runbook + handoff“Are we dependent on you forever?”
- 10Expansion map“What becomes possible if this succeeds?”
Every sprint ships behind an eval gate.
We agree the acceptance threshold before we build. Nothing reaches production on a demo and a good feeling.
Not included unless stated: large-scale data migration, major legacy-system redevelopment, certification audits, undefined organization-wide “AI transformation”, or dependencies controlled by third parties.
See the full sprint scopeDiscovery. Integration. Evals. Production. Handoff.
The same five stages every time. The first engagement is bespoke; by the third, most of the scaffolding is inventory we already have.
Map the workflow, not the wish
We sit with the people who run the process and map it end to end — trigger, normal path, exceptions, approvals, and the parts that only live in one person's head.
Build inside your architecture
We agree the design and control boundaries, then connect the real systems of record. No shadow stack, no synthetic sandbox standing in for production data.
Agree what “working” means
Task-success criteria, a failure taxonomy, groundedness checks and critical-risk tests — written down and thresholded before anything ships.
Cross the gate, with a way back
Controlled rollout behind the eval gate, with logging, human approval on high-impact actions, fallbacks and a rollback threshold defined in advance.
Leave your team self-sufficient
Your engineers can operate, debug and extend what we built. The goal is a system you own, not a dependency on us.
Your data stays your data.
Agent access, sensitive data and autonomous actions expand your risk surface. The controls should be explicit, observable and contractually bounded.
Your confidential inputs, outputs and data are not used to train or fine-tune any model made available to other customers, unless you authorize it in writing.
We retain our pre-existing technology and non-confidential generalized know-how. We do not hide cross-customer training rights inside language about “improving the service”.
PlatformBridge holds no SOC 2, ISO, HIPAA or FedRAMP status today. We describe the controls we actually operate, and we will tell you where a requirement sits outside our current posture.
- Customer access is provisioned on a least-privilege basis.
- Secrets are stored outside application code and prompts.
- Customer projects and credentials are logically segregated.
- Data is encrypted in transit and at rest where we store it.
- Material model providers and subprocessors are disclosed.
- Production actions are logged where technically feasible.
- High-impact or ambiguous actions can require human approval.
- Retention and deletion periods are contractually specified.
Owner, permitted use, data rights, model and subprocessor register, escalation.
Workflow, stakeholders, data, system boundaries, foreseeable failure modes.
Eval suite, security tests, quality, latency, cost, escalation, reliability.
Deployment gate, monitoring, human approval, incident response, rollback.
Built to become repeatable.
Once a workflow is understood, integrated and measurable, the repeatable portions become a persistent AI employee: it operates through your existing tools, applies explicit policies, measures its own task success, and escalates exceptions to people.
No magic autonomous headcount, and no learning across our other customers. An AI employee is a governed operating system around a model — which is why the surrounding layers matter more than the model choice.
What compounds between engagements is our own technology and non-confidential engineering know-how — never one customer’s confidential data showing up in another customer’s system.
Built by people who have carried the pager.
We are not a slide deck with an API key. Every engagement is run by engineers who have shipped production systems and stayed on to operate them.
Production, not pilots
Demos are easy. We optimize for the version that survives edge cases, on-call, and an audit six months later.
One accountable team, end to end
Discovery, architecture, code, integrations, evals and go-live sit with the same engineers. No handoff between the people who scoped it and the people who build it.
Workflow first, model second
We model the process you actually run, exceptions included. Which model you use is an implementation detail we will change if the evals say so.
Eval-gated, never vibes-gated
Acceptance criteria are agreed before the build starts, so “is it good enough?” is a measurement rather than an argument.
Reusable, not throwaway custom work
Every build is factored so the second and third deployment cost a fraction of the first. That compounding is the whole business model.
Your team stays in control
You keep the code, the runbook and the ability to operate it without us. A sprint that ends in dependency is a sprint we ran badly.
[PULL QUOTE — one or two sentences from a customer naming the workflow that went into production, the measured before-and-after, and how long the sprint took.]
Bring us the workflow that is stuck.
One operational process that is expensive, repetitive, and mostly living in people’s heads. In twenty minutes we will tell you whether it is a good first sprint, what the acceptance test should be, and what we would scope out.
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