Automate the 3-day process into 3 hours — without losing auditability.
Most manual workflows exist because no one trusts automation to handle the edge cases. We build AI workflows with tool allowlists, schema validation, eval suites, and human-in-the-loop escalation — so the process is faster and more auditable than the manual version it replaces.
Typically uses Azure OpenAI, Semantic Kernel, LangGraph, or your preferred orchestration layer.
What’s included
Multi-step orchestration
Workflows with conditional branching, parallel tool calls, retries, and dead-letter handling — not a single prompt-and-pray call.
Integration connectors
Pre-built and custom connectors for your CRM, ERP, database, and messaging systems. The workflow talks to your stack, not a demo environment.
Tool allowlists & schema gates
Every external call is gated by an allowlist and validated against a JSON Schema. The AI cannot invoke unapproved operations or produce malformed output.
Human-in-the-loop escalation
Configurable confidence thresholds route uncertain decisions to a human reviewer — before execution, not after damage.
Eval suite in CI
Regression tests, red-team prompts, and accuracy benchmarks run on every pull request. Regressions block the merge, just like unit tests.
Monitoring & cost dashboard
Latency P95, token cost per workflow run, error rate, and output drift — with alerting. Deployed alongside the workflow, not as an afterthought.
How we keep it safe
Typed integrations only
Every external call is defined as a typed tool with input/output schemas. Tools are registered in an allowlist. If it’s not on the list, the agent cannot call it — period.
Retries & dead-letter handling
Transient failures trigger automatic retries with exponential backoff. Permanent failures are logged with full context, alerted, and routed to a dead-letter queue for human triage. No silent failures.
Immutable audit trail
Every LLM call is logged: prompt, completion, token count, tool invocations, latency, and cost. Logs are append-only and queryable — ready for compliance review or incident forensics.
Quality you can measure
Eval suites gate every deploy
Golden-answer datasets, edge-case prompts, and accuracy thresholds run in CI on every PR. If eval scores drop, the merge is blocked. No regressions reach production.
Red-team testing on every release
A curated adversarial prompt set tests for jailbreaks, prompt injection, off-topic outputs, and data leakage. Runs automatically — not once in a slide deck, but on every release.
Live dashboards with alerting
Latency P50/P95, token cost per run, error rate, and output drift. Alerts fire when any metric breaches your threshold — you see the problem before your users report it.
Data & privacy
- Permissioning: role-based access controls determine which users and services can invoke each workflow.
- PII handling: configurable PII detection and redaction in prompts and logs — compliant with your data-handling policy.
- Data boundaries: your data stays in your tenant. We configure Azure OpenAI deployments in your subscription with your network controls.
Timeline & investment
Blueprint
10 days
Architecture + backlog
Build
4 – 8 weeks
MVP to production
Investment
$30K – $120K
Depends on scope
What we need from you
- • A designated product owner with decision-making authority
- • Access to the systems and APIs the workflows will integrate with
- • Sample data or test accounts for eval-suite development
- • Weekly 30-minute check-ins during the build phase
Security & guardrails your CISO will approve
Every AI system we ship includes these controls — in the first deploy, not a future phase.
Tool-call allowlists
The AI can only call tools you explicitly approve. Every external integration is registered with typed schemas — no unapproved operations, no unstructured side effects.
Schema-enforced outputs
Every response to a downstream system is validated against a JSON Schema before delivery. Malformed output is caught and logged, not silently propagated.
Eval suites in CI/CD
Regression tests, red-team prompts, and accuracy benchmarks run on every pull request. If eval scores drop below threshold, the merge is blocked.
Production observability
Latency P50/P95, token costs, error rates, and output drift — all in dashboards with configurable alerts. You see problems before users report them.
Human-in-the-loop gates
Configurable confidence thresholds route low-certainty decisions to a human reviewer before execution. The threshold is tunable without a code deploy.
Immutable audit trail
Every LLM call — inputs, outputs, token counts, tool invocations, cost, latency — is logged in an append-only store. Ready for compliance review or incident forensics.
Stop funding pilots that never ship.
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