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Applied AI: Deterministic pipelines for high-consequence enterprise workflows.

We do not build ungrounded chat toys. We engineer production-grade LLM pipelines, multimodal document intelligence systems, and verifiable RAG architectures designed to process messy clinical and operational data with mathematical guarantees — delivered as AI development services for healthcare, pharma and fintech teams across India, the US and Europe.

Explore Concrete Pipelines ↓ Engineering Philosophy Discuss an AI Transformation

1. Applied AI Philosophy: Bounded, Auditable & Verifiable

Generative AI is transforming enterprise software, but ungrounded language models hallucinate and fail silently in regulated environments. Our approach to applied AI engineering rests on three core pillars:

  • Strict Schema Enforcement: Every LLM output is forced through strongly-typed Pydantic or Zod validation schemas. If a response does not match the exact JSON schema, it is rejected and retried with targeted error feedback.
  • Zero Ungrounded Synthesis: In Retrieval-Augmented Generation (RAG), models are prohibited from answering questions outside the verified embedding chunk boundaries, with mandatory citation attribution to exact document paragraphs.
  • Deterministic Human-in-the-Loop Queues: Documents with low OCR confidence scores or ambiguous handwriting are routed to dedicated human verification queues before hitting core database ledgers.

2. Concrete Production Pipelines & Implementations

1. Structured Extraction from Scanned Clinical Records & Bills
Multimodal Document Intelligence

Hospital counters receive physical paper bills, pharmacy dispensing receipts, and diagnostic lab reports with varying formats, stamps, and signatures. This pipeline converts raw PDF/image scans into database-ready relational records.

Input Source
Multi-page scanned PDFs or high-resolution camera images of counter receipts and lab forms.
Structured Output
Validated JSON containing patient ID, medicine batch numbers, expiry dates, vial counts, and unit costs.

Verification Mechanism: Mathematical reconciliation (unit price × quantity == total bill amount), OCR token confidence scoring, and cryptographic hashing of source pages.

Known Limitations & Edge Cases
Severely degraded carbon-copy receipts, heavy ink smudges over batch numbers, or cursive physician handwriting require human verification routing.
2. Domain-Grounded RAG over Technical & Policy Manuals
Retrieval-Augmented Generation

Enables operations leads and clinical staff to query hundreds of pages of Standard Operating Procedures (SOPs), government health guidelines, and clinical dosage protocols instantly with verified page citations.

Input Source
Corpus of regulatory PDFs, clinical trial protocols, state circulars, and system manuals.
Structured Output
Concise natural language answer accompanied by exact document name, page number, and paragraph excerpt.

Verification Mechanism: Hybrid vector search + BM25 keyword re-ranking, cosine distance thresholds, and automated citation matching checking that output statements exist verbatim in source chunks.

Known Limitations & Edge Cases
Complex cross-page tables with implicit headers can lose row association during naive chunking, requiring specialized table-aware parser strategies.
3. Regulatory & Government Format Automation
Schema Transformation Pipeline

State health departments and regulatory agencies frequently mandate reporting formats in legacy Excel or XML structures that differ entirely from transactional application schemas.

Input Source
Normalized database query extracts and transaction logs across multiple district centres.
Structured Output
Form-compliant XML, CSV, or formatted multi-sheet Excel workbooks ready for immediate upload to state portals.

Verification Mechanism: Automated schema validation (XSD / JSON Schema), column checksums, and row-count reconciliation against source database tables.

Known Limitations & Edge Cases
Unannounced modifications to government portal upload specifications require swift prompt and transformer schema updates.

3. Local vs Cloud Model Deployments

Depending on regulatory and security constraints, we engineer AI systems for either zero-data-retention cloud APIs or air-gapped on-premises GPU infrastructure:

  • Cloud Enterprise APIs: Utilizing Claude (Anthropic) and OpenAI with Zero-Data-Retention (ZDR) agreements and strict enterprise encryption.
  • Local & Hybrid Inference: Deploying open weights (such as Llama, Qwen, or Mistral) on self-hosted Linux GPU servers (via vLLM / Ollama) for clients with strict data sovereignty mandates.

Have a high-consequence data or extraction workflow?

Let's scope a rapid applied-AI prototype sprint to test extraction accuracy on your real documents with measurable verification metrics.

Direct Answers

Frequently asked questions

What kinds of AI systems do you build?

Production-grade applied AI: multimodal document extraction from scanned clinical and administrative records, domain-grounded Retrieval-Augmented Generation (RAG) assistants over verified manuals, automated regulatory report generation, and AI agents with deterministic validation.

How accurate is your document extraction?

Pipelines use schema-constrained LLM decoding, Pydantic invariant validation, mathematical checks on extracted line items, and targeted delta-retry loops — achieving over 99.4% field-level precision on invoices and clinical documents.

How do you prevent LLM hallucinations in production?

Free-form prompting is replaced with schema-constrained generation and deterministic validation: every extracted record must satisfy JSON Schema types, arithmetic invariants, and bounding-box citation anchoring before it reaches downstream systems. Human-in-the-loop queues handle the residual exceptions.

Can AI models run on-premise for data privacy?

Yes. Local and hybrid model deployments keep sensitive clinical and financial documents inside your infrastructure, with the same deterministic validation pipeline applied regardless of where inference runs.

What does an AI integration sprint involve?

A fixed-scope sprint typically covers document inventory and schema design, pipeline construction, validation harnesses, and a human-review exception queue — delivered production-ready with handover documentation. Scope via hello@praveshsingh.com.