Inheritance — AI — Smart Contracts
10102
Digital Inheritance Platform — UI/UX overhaul with AI-augmented flows purpose-built around a sensitive, multi-party business process.
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AI ServicesCustom AI Workflow
Custom AI Workflow
Solutions
Off-the-shelf AI tools solve generic problems generically. When the workflow is specific, the data is specific, and the quality bar is specific, you need a custom solution.
Engagement
AI Solutions
Typical Duration
6 – 12 weeks
Purpose-built AI systems designed around a specific business process. Extraction, classification, generation, and reasoning combined with business logic, data integrations, and operational controls. Not just one step of the workflow. End-to-end.
Common patterns
Document Processing Pipelines
PDF in, structured data out. Extract key fields, cross-reference against databases, flag anomalies, auto-process or queue for review. Seconds, not hours.
Classification & Triage
Incoming items classified by type, urgency, complexity. Confidence-scored. AI that understands context, not just keyword matching.
Content Generation
Reports from data, summaries from documents, responses from context. Constrained by templates, business rules, and quality checks.
Decision Support
AI assembles information, evaluates against criteria, presents a recommendation with reasoning. Human makes the final call.
Compliance Checking
Automated review against standards, regulations, or business rules. Style compliance, regulatory requirements, consistency validation.
Multi-Step Orchestration
Multiple AI capabilities chained with conditional logic, branching, error handling. Each step feeds the next with monitoring throughout.
How we build
Process Mapping — Step 1
Document every step, decision point, exception. Identify which steps AI handles, which need oversight.
01
Process Mapping — Step 1
Document every step, decision point, exception. Identify which steps AI handles, which need oversight.
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Solution Architecture — Step 2
AI pipeline, data integrations, human touchpoints, error handling, monitoring.
Solution Architecture — Step 2
AI pipeline, data integrations, human touchpoints, error handling, monitoring.
Iterative Development — Step 3
Build in stages, test with real data at each stage. Validate quality early.
03
Iterative Development — Step 3
Build in stages, test with real data at each stage. Validate quality early.
04
Evaluation — Step 4
Measured against defined targets. Accuracy per field, per category, against human performance.
Evaluation — Step 4
Measured against defined targets. Accuracy per field, per category, against human performance.
Production Deployment — Step 5
With monitoring, alerting, and operational runbooks.
05
Production Deployment — Step 5
With monitoring, alerting, and operational runbooks.
Deliverables
What you get
- Production-ready AI workflow system
- Integrations with existing systems
- Human-in-the-loop interfaces where applicable
- Quality benchmarks against defined targets
- Monitoring dashboards and alerting
- Operational runbooks and improvement playbook
LLMsDocument ProcessingClassificationOrchestrationTemporalClaudeGPT-4Custom AI
Have a specific process that generic AI tools can’t handle properly?
We build AI that fits into real operations. Not demos that work with clean data in controlled conditions. If the workflow is specific and the quality bar is high, that’s where we work best.