Automation
Automation that survives audits, incidents, and staff turnover.
The failure mode is rarely “the model was wrong.” It is unowned logic, invisible retries, coupling that hides errors, and spend no one can trace. We engineer workflows as systems: explicit states, bounded failure domains, and evidence that ties each run to identity, data touched, and controls—so security, IT, and operators share the same story.
What this practice covers
Orchestration across people, APIs, files, queues, and models
We design and build long-running business processes that span systems owned by different teams: ERP, CRM, ticketing, data warehouses, custom services, SaaS with uneven APIs, and—where it genuinely helps—language models or classifiers. The through-line is always the same: predictable state transitions, idempotent side effects where possible, and a clear owner when something lands in a dead-letter queue.
Engagements range from greenfield automations (new product or operations initiative) to hardening what already runs in a scheduler or low-code tool—especially when production behavior diverged from the deck, or when the person who “knew how it worked” left.
| Class of work | Examples | What “done” looks like |
|---|---|---|
| Case & ticket intelligence | Routing, summarization, enrichment, suggested replies—always with escalation paths and sampling for quality control. | Measured precision/recall on held-out cases, audit trail per ticket, rollback of model version without redeploying the whole product. |
| Document & record intake | PDFs, scans, forms, emails → structured fields with validation rules and exception queues for low-confidence extractions. | Human review SLAs defined, retention policy explicit, lineage from source file to downstream tables. |
| Financial & operational reconciliation | Multi-source matching, variance thresholds, scheduled closes, notifications when drift exceeds policy. | Checksums and reconciliation reports your controllers can file; alerts tied to business thresholds, not only HTTP 500s. |
| Provisioning & access workflows | Joiner/mover/leaver patterns, approvals, integration with IdP and ITSM—without handing everyone admin keys. | Least-privilege service accounts, break-glass documented, periodic access reviews supported by automation evidence. |
| Data pipeline orchestration | Dependencies, SLAs, failure isolation between bronze/silver/gold layers, and coordination with infrastructure for compute and secrets. | Data contracts, backfill strategy, and incident playbooks that infra and data teams both recognize. |
Reality check
Failure modes we design against from day one
Silent partial success
Individual steps return 200 while the business outcome is wrong—classic with eventual consistency and duplicate submissions. We use checkpoints, compensating actions, reconciliation jobs, and human gates when ambiguity is material.
Unbounded model cost & drift
Token spend, latency, and prompt drift compound without guardrails. We ship budgets, caching where ethical, evaluation hooks, versioned prompts/policies, canary releases, and deterministic fallbacks—not “prompt in prod and hope.”
Audit without narrative
Regulators, insurers, and internal risk teams ask what happened and why. Every run needs correlation IDs, actor identity, inputs/outputs (with redaction rules), model version, and retention posture tied to a named control.
Engineering patterns
How runs are structured—not which logo is on the box
We are intentionally vendor-neutral in public copy: your stack might be Temporal, Airflow, Step Functions, n8n, custom code, or a mix after acquisitions. What we insist on is engineering discipline—so you are not locked into our proprietary runtime.
Orchestration & state
Explicit workflow state, timeouts, retries with jitter, idempotency keys, sagas or compensations where money or inventory moves, and dead-letter handling with a defined triage owner.
Integration boundaries
Contracts per dependency (schemas, rate limits, error taxonomies), circuit breakers, and versioned adapters so a vendor API change becomes a contained patch—not a production mystery.
Human-in-the-loop
Approvals, sampling, escalation tiers, and SLAs for exception queues. The goal is rarely full autonomy; it is faster, safer throughput with operators still accountable.
Secrets & identity
Workload identity, short-lived credentials, rotation patterns, and segregation between build pipelines and production—so automation is not the back door past your IdP.
Delivery standard
Artifacts you keep after we leave
These are concrete deliverables—not aspirations. They are how new hires and auditors get productive without reverse-engineering YAML.
- 01
Control narrative
Plain-language map of triggers, data classes touched, blast radius, retention, and who approves exceptions—aligned to how your risk committee actually reads.
- 02
Test & evaluation set
Representative cases, regression suite, property-based checks where useful, and red-team prompts / adversarial inputs when models are in the path.
- 03
Runbook & on-call contract
What “green” means, how to roll back, escalation paths, vendor contacts, and which integration owner answers at 2 a.m.
- 04
Observability contract
Metrics, structured logs, traces where warranted, and alerts wired into your existing stack—thresholds tied to business impact, not only infrastructure CPU.
- 05
Change & release record
How promotions work across environments, who can approve, and how configuration drift is detected before it becomes an incident.
From pilot to production
Pilots are bounded by explicit non-goals, success metrics, and promote/pause criteria. We avoid “successful demos” that cannot inherit your real identity model, data volumes, or approval chains. Production cutover includes load assumptions, rollback rehearsal, and a communications plan for operators.
Operate & improve
Automation rots when no one owns backlog hygiene. We set up review cadences for prompts, dependencies, and data drift; incident retros that feed the test suite; and capacity planning when scheduled work competes with ad hoc replays.
Scope
What we are not trying to be
We do not sell a proprietary “AI operating system” that replaces your stack. We integrate with tools you already pay for, document boundaries honestly, and measure impact in throughput, defect rates, time-to-recover, and cost per outcome—not vanity automation counts.
We also do not promise magic headcount removal on day one. Sustainable automation reallocates attention; it still needs owners, budgets, and change management.
If your automation cannot be explained to your risk committee, it is not ready for production—only for a pilot folder.
Related
Automation consumes data you trust and runs on infrastructure you can recover. For adjacent depth, see Data, Infrastructure, and IT services. For how practices combine in one program, see Solutions.
To scope an engagement, use Consultation or Contact.