Strategy
Deciding what to build, before a line of code.
We work with leadership teams on AI strategy: use-case discovery, target architecture and an honest read on build versus buy.
Explore StrategyWe are an AI engineering firm based in San Francisco. We design the system, build it, run it in production, and stay until your team can operate it.
Input
Loan application
- Application form
- Pay stubs, 3 months
- Bank statements, 6 months
- Credit report
System Complete
Loan file review
- Read 10 documents
- Retrieved the lending policy
- Compared stated and verified income
- Ran policy and evaluation checks
Result
Needs review
Stated income is 18% above the verified pay stubs. Everything else meets policy.
Sources
- 1 Pay stub, March
- 2 Lending policy §4.2
Examples of the kind of system we build. Pick one to see it run.
Most AI projects never make it past the demo.
The model is rarely the problem. Everything around it is: the data, the security, the testing, and running it every day.
That is the work we do.
What we do
Data and Platform
The platforms, data and cloud your AI stands on.
AI is only as good as the estate beneath it. We build the data pipelines, model serving, cloud architecture and observability that keep systems running, on infrastructure your own team can operate.
Explore Data and PlatformEngineering
Production AI systems, built by senior engineers.
This is the core of the firm: agents, retrieval, copilots, decision systems and the software around them.
Explore EngineeringAdoption and Enablement
Your people, fluent in the systems we build.
A system nobody adopts is a failed system. Training, runbooks and working sessions with your engineers and operators are deliverables in every engagement, so the capability stays in your organization after we leave.
Explore Adoption and EnablementCustomer Experience
AI your customers talk to, answering in your name.
We build customer-facing AI: service copilots, assisted support and retrieval over your own knowledge. Each system ships with the evaluation suite that keeps its answers accurate as models and content change.
Explore Customer ExperienceCybersecurity
AI engineered to pass your security review.
We design AI systems for enterprise risk: threat modeling, access controls and audit trails from the first architecture.
Explore CybersecurityFinance Operations
AI for the finance function, built to survive an audit.
We build decision support, document retrieval and workflow automation for finance teams, engineered for auditability from the start.
Explore Finance OperationsManaged Operations
Keeping the system right after launch, as the models move.
Launch is not the finish of an AI system. Models are revised and retired, data and content move, and the business changes what a correct answer looks like.
Explore Managed OperationsForward Deployed Engineering
Senior engineers inside your organization, still accountable to us.
Some work cannot be written into a fixed scope, because the problem is still moving while you describe it.
Explore Forward Deployed EngineeringWhat we have built
Before Caltor, the founders built and ran their own companies. These six ventures are that work. They are our own products, not client projects.
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Miravio
Healthcare SaaS
A clinical operations platform for behavioural-therapy practices in the United States.
Shows our work in Data and Platform -
Antenna
Open-source SaaS
A social media studio for organizations that run many brands across many networks.
Shows our work in Customer Experience -
Redwipe
Cybersecurity
An attack-surface management platform for security teams.
Shows our work in Cybersecurity -
Pays
Agentic fintech
A financial operating system for AI agents, built as a working prototype.
Shows our work in Finance Operations -
Perdais
LegalTech
A UK Global Talent visa platform for candidates, attorneys and case teams.
Shows our work in Engineering -
Zilk
AI & network engineering
An AI, cloud, network and security engineering firm, operating today with 450 defined services.
Shows our work in Managed Operations - View experience
How a project runs
Typical consultancy
Caltor
2–3x faster to production
Handoffs between separate teams One senior team, stages overlap Illustrative comparison.
2–3x
Faster to production
Caltor typically takes a project to production two to three times faster than a traditional consultancy. The partner who scopes the work leads it, senior engineers are on it from the first day, and nothing is handed from a strategy team to a delivery team. Your organization gets its time back.
Where the time comes from
- 1 The partner who scopes the work leads it, start to finish.
- 2 Senior engineers from the first day, never a junior bench.
- 3 One team from strategy to production: no handoffs between firms or departments.
Discover
We learn the business problem, who it affects, what it costs you and what limits the solution.
Why it is faster The partner who scopes the work is in the room, so nothing is discovered twice.
Design
We choose the architecture and the AI approach, and agree the plan with you.
Why it is faster The engineers who design the architecture are the ones who build it, so nothing is re-explained.
Build
We build the software and the AI system. You see working parts as they are finished.
Why it is faster Senior engineers only, with evaluation from the first increment, so there is little rework.
Deploy
We put it into production with security, integrations and monitoring in place.
Why it is faster Security and integrations are designed in from the start, not found in a late review.
Evolve
We keep improving it after launch, or hand it to your team with everything they need to run it.
Why it is faster Handover is planned from day one, so your team takes over without a transition project.
Illustrative: what each stage produces.
Industries we work in
Sectors where regulation, integration and the cost of a wrong answer decide how a system must be engineered.
Banks, payments, insurance and fintech
Financial services
We build AI systems that can survive an audit: decision systems, retrieval over regulated documents, and the controls around both. The founders designed payments infrastructure where every payment is bound to an auditable spend policy, and built security tooling that reports against SOC 2, ISO 27001 and PCI-DSS. That is the discipline we bring to banks, insurers and fintechs.
What we build
- Retrieval over regulated documents, every answer cited to its source
- Decision support for credit, underwriting, servicing and operations
- Agents for finance and payment workflows, with human approval
- Integration with core banking, payment, ledger and CRM platforms
OEMs, suppliers, mobility and retail
Automotive
Automotive AI lives inside an estate of existing systems: ERP, dealer platforms, telematics and supplier data. We build the integration layer and the AI on top of it, from copilots for sales and service operations to decision systems over vehicle and warranty data. Every build is engineered for the systems already in place, not for a clean-room demo.
What we build
- Integration across dealer, warranty, parts and vehicle systems
- Copilots for sales, service and aftersales teams
- Warranty and quality systems with an evidence trail behind every decision
- Pipelines carrying plant, supplier and logistics signals to where they are acted on
Manufacturing, logistics, ports and infrastructure
Industrial
Manufacturing and logistics generate operational data that rarely reaches the systems that could act on it. We build the pipelines that move that data and the AI systems that use it: monitoring, scheduling and decision support connected to the platforms already running the operation. The founders built and operate an attack-surface management platform, so exposure assessment is part of how we design for industrial estates, not an add-on.
What we build
- Pipelines for sensor, telemetry and work-order data
- Condition monitoring that shows the readings behind every flag
- Planning and dispatch support for logistics and field operations
- Retrieval across inspection and compliance documents, traceable to the page
Providers, life sciences and health platforms
Healthcare
The founders built and operate a clinical operations platform for behavioural-therapy practices in the United States: regulated clinical data, scheduling, billing and family communication, with strict per-practice data isolation. That platform sets the bar for our healthcare work, where interoperability standards and HIPAA obligations are design inputs from the first architecture. We build AI systems for providers, life sciences and health platforms to the same standard.
What we build
- Retrieval and drafting over administrative records, sources shown
- Intake, scheduling and follow-up workflows
- Billing, claims and prior authorization support, with a person deciding
- Access control, consent, tenant separation and audit logging by design
Large organizations with complex software estates
Enterprise technology
Large organizations rarely need another point tool; they need AI that works inside the identity, ERP, CRM and data platforms they already run. We design for that estate first, then build the system: agents, retrieval and workflow automation, with evaluation, security review and observability in the architecture rather than added later. Handover is part of every build, so your team operates what it owns.
What we build
- The AI feature itself, built into your product and codebase
- Evaluation suites as acceptance criteria, run on every change
- Data and serving layers with tenant isolation
- Security architecture written for your customers' review
Technologies we build with
The models, clouds and platforms we put into production.
AI and models
- Anthropic
- OpenAI
- Google Gemini
- Meta
- Hugging Face
- LangChain
- Ollama
- PyTorch
- TensorFlow
- scikit-learn
- NVIDIA
- MLflow
- Weights & Biases
- ONNX
Cloud
- AWS
-
Microsoft Azure - Google Cloud
- Cloudflare
- Oracle Cloud
- DigitalOcean
- Vercel
Data
- PostgreSQL
- MongoDB
- Snowflake
- Databricks
- Google BigQuery
- Apache Spark
- Apache Kafka
- Apache Flink
- Elasticsearch
- ClickHouse
- Neo4j
- DuckDB
- Redis
- dbt
- Apache Airflow
- Supabase
Platform and delivery
- Kubernetes
- Docker
- Helm
- Argo
- Terraform
- Ansible
- Temporal
- Prometheus
- Grafana
- Datadog
- OpenTelemetry
- Sentry
- GitHub Actions
- GitLab
- Jenkins
- NGINX
- RabbitMQ
- Linux
Security and identity
- Okta
- Auth0
- HashiCorp Vault
- Snyk
Languages and frameworks
- Python
- TypeScript
- Go
- Rust
- Java
- Kotlin
- Swift
- .NET
- Node.js
- FastAPI
- Django
- React
- Next.js
- Vue.js
- Svelte
Talk to a partner
Tell us the problem. We'll come back with an architecture, a scope and a number. Most work with us starts small: one fixed-scope piece of work that proves the system before anything larger is committed.