Automation & applied AI
Automation first.Models where they earn their keep.
We map brittle manual steps, then wire APIs, queues, and (when appropriate) LLM-backed helpers — with logging, human review, and cost ceilings you can defend in finance.
Process before prompts
If the workflow is undefined, automation only ships confusion. We document states, owners, and exceptions first.
Honest metrics
Hours saved, error rates, or tickets deflected — picked with you, measured with the same analytics stack as the rest of the site.
Thin slices
One painful loop automated end-to-end beats a roadmap slide that never reaches production.
Capabilities
What we actually ship
Written acceptance criteria, visible milestones, and owners named on day one.
Workflow automation
Reliable orchestration between CRMs, ERPs, billing, and internal tools — with idempotency and dead-letter handling.
Key Deliverables
- Event-driven or scheduled jobs
- Approvals and exception queues
- Secrets via vaults — not pasted keys
- Alerting when a run stalls
Document & data helpers
Classification, extraction, and routing where ML beats regex — with ground-truth samples and regression checks.
Key Deliverables
- Structured outputs with schema validation
- Retrieval constrained to approved corpora
- Confidence thresholds + manual fallback
- Versioned prompts and evaluation sets
Assistants (internal-first)
Staff-facing copilots over your handbook, SOPs, and ticket history — not public chatbots trained on guesses.
Key Deliverables
- Source citations required in answers
- Role-based access to knowledge
- Audit trail of queries (configurable)
- Escalation paths to humans
Integration & data plumbing
The boring pipes that make models useful: sync jobs, deduping, and observability across environments.
Key Deliverables
- ETL/ELT with failure replay
- API middleware for legacy systems
- Feature flags for gradual rollout
- Staging datasets that mirror prod shape
Technology Stack
Outcomes
Examples from client work
Figures are directional; we'll share context on a call under NDA where needed.
Typical window for a single high-friction internal workflow once APIs and approvals exist.
We default to your cloud tenancy and contracts; no model training on your data unless explicitly agreed.
Feature flags and rate limits so a bad deploy does not become a billing or PR incident.
Why Thorium
The Thorium
Difference
Principals stay involved. We do not park you with a rotating bench of juniors.
Model choice is tactical
We match latency, cost, and compliance to the task — not the trendiest badge on a slide.
Security review on auth paths
Anything that touches PII or credentials gets the same scrutiny as a customer-facing app.
Works with your stack
Salesforce, HubSpot, Google Workspace, Microsoft 365 — integrated with realistic field mappings.
Operational handoff
Runbooks and on-call expectations are written for your team, not only ours.
How It Works
Our Proven Process
Weekly checkpoints, shared backlog, and change requests in writing — so scope stays legible.
Phase 1
Map & measure
Interview operators, quantify minutes per run, and list failure modes before touching code.
Phase 2
Design guardrails
Human checkpoints, data redaction rules, and rollback for partial automation.
Phase 3
Pilot
Shadow mode or limited cohort until error rates sit where you are comfortable.
Phase 4
Operate
Monitoring dashboards, cost reports, and quarterly pruning of unused prompts or jobs.
Much of our portfolio is confidential. After a short intro we'll share redacted examples that match your sector and risk profile.
Washington, DC · Remote-friendly
Common Questions
Frequently Asked Questions
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