Process before prompts
If the workflow is undefined, automation only ships confusion. We document states, owners, and exceptions first.
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.
Trusted by teams in DC & remote-first orgs
No obligation · Response within 1 business day · NDA available
Why teams hire us
- Principal engineers on your account
- Written scope before build starts
- Weekly demos & shared backlog
- NDA-friendly from first call
API-first
Integration style
Human review
Default for risk
Usage caps
Token & spend
Runbooks
On failure paths
API-first
Integration style
Human review
Default for risk
Usage caps
Token & spend
Runbooks
On failure paths
What you get
Built for operators, not demos
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.
Let's scope your project in one call
Bring your timeline, stack, and stakeholders. We'll tell you honestly if we're the right fit and what a first milestone could look like.
Capabilities
What we actually ship
Written acceptance criteria, visible milestones, and owners named on day one.
01
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
02
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
03
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
04
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
OpenAI / Anthropic APIsPythonFastAPIn8nNode workersPostgres / pgvectorAWS LambdaVercel AI SDK
Outcomes
Proof in the work
Directional benchmarks from real engagements — details on a call under NDA when needed.
2–4 wk
First automation
Typical window for a single high-friction internal workflow once APIs and approvals exist.
Data stays
In your boundary
We default to your cloud tenancy and contracts; no model training on your data unless explicitly agreed.
Kill switch
Designed in
Feature flags and rate limits so a bad deploy does not become a billing or PR incident.
Why Thorium
The Thoriumdifference
Principals stay on the thread. Scope, owners, and weekly visibility are written down — so you are not guessing what shipped.
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 provenprocess
Weekly checkpoints, shared backlog, and change requests in writing.
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.
Selected work
Recent client work
FAQ
Common questions
Do we need a data science team?
No. Most engagements are integration and software engineering with occasional model tuning — not research projects.
Will you train on our documents?
Only if you request it and legal approves. Default posture is retrieval against private indexes without retaining content for training.
What about hallucinations?
We constrain outputs, require citations where possible, and keep humans on anything that affects money, safety, or regulatory filings.
Can this run on-prem?
When policy requires it, we deploy containers or use VPC-hosted inference. That tradeoff is cost and velocity — we spell it out early.
How is it priced?
Milestone fees per workflow or retainer for iteration. Usage-based AI spend is passed through at cost or capped in the SOW.
Work with Thorium
Ready whenyou are
Tell us what you need to ship. We respond within one business day with next steps — not a generic pitch deck.