AI Engineering for Real-World Workflows
We build production-grade AI systems that convert complex, high-volume data into reliable, auditable outputs. From intelligent document processing to LLM-powered automation, we ship systems designed to scale.
Core Capabilities
Practical, implementation-focused features that support enterprise deployments—accuracy, control, and maintainability included.
Intelligent Document Processing (IDP)
OCR, layout understanding, classification, and extraction for real-world documents.
- PDF/image ingestion with robust OCR handling
- Forms, tables, headers, footers, and multi-column parsing
- Entity extraction, normalization, and deduplication
LLM Engineering
Reliable LLM outputs through schema-driven prompting and evaluation.
- Strict JSON outputs validated against schemas
- Prompt libraries, templates, and regression testing
- Multi-vendor strategies to reduce lock-in
Custom AI Pipelines & Orchestration
Multi-stage workflows with validation, fallbacks, and observability.
- Deterministic-first logic with AI augmentation
- Retry/backoff, model fallback tiers, and guardrails
- Full audit trail from input → output
Semantic Search & Retrieval
Embeddings-based search and retrieval for knowledge-rich workflows.
- Vector search, hybrid search, and re-ranking
- Source-cited answers and traceable references
- Corpus updates, incremental indexing, and tuning
Knowledge Systems
Cross-document correlation and structured intelligence layers.
- Knowledge graphs and entity resolution
- Timeline generation and event linking
- Confidence scoring and conflict detection
Security, Privacy & Compliance
Designed for sensitive data with governance and traceability.
- PII-aware handling and redaction workflows
- Access controls, logs, and environment isolation
- Compliance-aligned design for regulated domains
Production Readiness
Engineering that supports uptime, monitoring, and maintainability.
- Observability: metrics, tracing, and error reporting
- CI/CD integration and environment promotion
- Performance tuning and load-aware design
Cost Optimization
Model routing and caching to control spend without sacrificing quality.
- Cache layers for repeated calls and stable outputs
- Task-based model selection (fast/cheap vs deep)
- Batching, chunking strategies, and token reduction
How We Build
We prioritize reliability and auditability—especially in high-stakes workflows where correctness matters.
Engineering Principles
- Deterministic foundations: rules and parsing first, AI where it adds measurable value.
- Human-reviewable outputs: structured data and traceable sources to support QA.
- Fail-safe behavior: retries, fallbacks, and validation to reduce silent failures.
- Replaceable components: modular architecture for swapping vendors/models.
- Production from day one: observability, security, and performance baked in.
Delivery Artifacts
Typical outputs we ship as part of an engagement:
- Working web apps or internal tools
- ETL/IDP pipelines with dashboards
- LLM services with schema validation
- Search + knowledge layers (RAG)
- Documentation + handoff support
Industries & Use Cases
We focus on applied AI—where results are measurable and deployments have operational impact.
Medical & Healthcare Ops
Clinical document extraction, coding support, and workflow automation.
Legal & Case Review
Record organization, timeline generation, and evidence indexing.
Insurance & Claims
Automated intake, document intelligence, and structured summaries.
Enterprise Knowledge
Internal copilots, search, and retrieval systems over private data.
Engagement Model
Whether you need a targeted build or a long-term engineering partner, we align delivery to business outcomes.
Prototype → Production
Rapid proof-of-value with a clear path to production hardening.
- Scope definition, data review, and success metrics
- Working prototype with measurable evaluation
- Productionization: reliability, security, scale
Project-Based Builds
Fixed deliverables with transparent milestones and documentation.
- Architecture, implementation, and deployment
- Testing strategy and quality gates
- Handoff and operational runbooks
Optimization & Ongoing Support
Continuous accuracy improvements, cost tuning, and monitoring.
- Model routing improvements and caching
- Evaluation harnesses and regression testing
- Performance tuning and incident support
Build AI That Works in the Real World
If you have documents, workflows, or internal systems that need intelligent automation, we can help you move from concept to production with clarity, control, and measurable outcomes.
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