How we ship automation and AI under real constraints
We build automation-first systems that hold up in production, with reliability, privacy, and cost control designed in from day one.
- Clear operational scope, measured outcomes, and firm guardrails
- Automation that reduces risk, not just workload
What we mean by automation first
We design the product around automated execution, so humans handle exceptions and decisions, not repetitive work.
Definition
- Clear inputs and outputs with auditable logs
- Rules and policies encoded as testable logic
- Fallbacks when confidence or data quality drops
What we do not mean
- Replacing domain experts without review steps
- Black box decisions without traceability
- Automations that bypass legal or policy checks
Where AI fits and where it does not
Use cases
- Classification and routing with measured confidence
- Summaries with source links and review checkpoints
- Data extraction with validation rules
- Operator copilots for faster decisions
Anti use cases
- Final decisions with legal or financial impact
- Unbounded free text outputs sent to customers
- Core logic with no deterministic fallback
- Systems without monitoring or rollback
The reliability stack
Evaluation
- Golden datasets with real edge cases
- Precision and recall targets tied to business risk
- Regression tests on every release
Guardrails
- Policy checks before execution
- Confidence thresholds for automation
- Structured outputs and schema validation
Fallbacks
- Deterministic rules when AI is unsure
- Queueing for manual review
- Graceful degradation when data is missing
Human review
- Sampling plans for oversight
- Audit trails with decision history
- Escalation paths with clear SLAs
Data, privacy, and security
- Data classification and explicit retention windows
- Encryption in transit and at rest by default
- Role based access and least privilege policies
- Redaction and tokenization for sensitive fields
- Audit logs for model inputs, outputs, and user actions
Cost and performance
- Unit economics tracked per request and per workflow
- Latency budgets defined with clear fallbacks
- Monitoring for cost spikes, drift, and error rates
- Batching and caching where accuracy allows
Delivery process: Sprint then Build
Sprint
- Problem framing and risk mapping
- Target metrics and reliability thresholds
- Prototype with real data and evaluation report
- Go or no go decision with scope and budget
Build
- Production architecture and API contracts
- Guardrails, monitoring, and alerting setup
- Operator tooling for review and overrides
- Staged rollout with acceptance criteria
Two mini case examples
Logicare
Problem: Regulated billing rules vary by profession, change over time, and require strict guardrails.
Approach: A rules engine with versioned policies, simulation mode, and human review for exceptions.
Result: Faster claim validation with fewer manual checks and a clear audit trail.
Risks handled: Rule conflicts, evolving regulations, and traceability requirements.
Sportero
Problem: High volume consumer data ingestion with frequent feedback loops and automation at scale.
Approach: Streaming pipelines, validation gates, and automated tagging with manual overrides.
Result: Reliable automation that scales without degrading user experience.
Risks handled: Data drift, noisy inputs, and cost spikes during peak usage.
Engagement options
Weel Build
We ship automation for operators with a clear scope, delivery timeline, and fixed ownership. No equity.
- Defined deliverables and acceptance criteria
- Operational handover with training
- Optional maintenance and monitoring
Weel Founding
We co found one company per year, with equity based on the project and execution load.
- Shared leadership and long term commitment
- High bar for distribution and market access
- Ownership aligned with execution risk
FAQ
How accurate are the systems
We set accuracy targets based on risk and validate against real datasets before production rollout.
How long do you keep our data
Retention windows are defined contractually, with deletion and audit policies enforced by default.
Can you support compliance reviews
Yes. We document data flows, access controls, and evaluation methods as part of the delivery.
What timelines should we expect
Sprints typically run a few weeks. Build timelines depend on scope, data readiness, and integration needs.
Who owns the IP
Build projects are client owned. Founding projects are shared and defined by the co founding agreement.
What about maintenance
We offer ongoing monitoring and improvement plans, or we can hand over to your internal team with full documentation.
Ready for a serious automation partner
If you need reliable automation under real operational constraints, we can scope a sprint or evaluate a co founding fit.