From PDF to App: An Agentic Workflow for Document-Driven No-Code Apps

Executive Snapshot
Challenge: A leading no-code platform struggled to transform specification PDFs (contract templates, regulatory forms) into live apps without manual coding.
Solution: 8tomic Labs built a cloud-native agentic pipeline—ingesting PDFs, parsing layout, auto-generating form schemas, and injecting a runtime widget—using LangGraph, Llama3-27B, Tesseract+LayoutLM, pgvector, and Kubernetes-hosted microservices.
Impact: Reduced form-launch time by 90%, achieved 98.7% field-extraction accuracy, and cut developer effort by 75%.

1. Client Context & Problem

The client offers a visual no-code builder where citizen developers drag-and-drop to create business apps and dynamic forms. Yet, turning static documents—like PDF spec sheets, SLA contracts, or compliance checklists—into interactive forms required manual field mapping, custom code, and days of effort.

Pain Points:

1. Manual Effort: Average form creation took 3 days of developer time per template.
2. Error-Prone: Hand-coded schemas led to 15% field mismatch rates in UAT.
3. Inconsistent UX: Each form looked and behaved differently based on developer style.

ELI-5: Converting a PDF to a form is like teaching a robot to read a recipe and then cook the meal automatically—most teams still chop the veggies by hand.

Understanding the struggle of manual conversion highlights why an automated, agent-driven solution was essential.

2. Agentic Workflow Overview

Our agentic pipeline treats document-to-app conversion as a series of autonomous AI steps:

1. Ingest PDF: Receive spec PDFs via a secure upload service.
2. Parse & OCR: Use Tesseract for basic OCR, then LayoutLM for block detection—headings, paragraphs, tables, form fields.
3. Schema Generation: An LLM agent (Llama3-27B) maps detected fields to JSON schema definitions.
4. UI Rendering: Generate form components (text inputs, dropdowns, tables) via a dynamic API.
5. Runtime Guidance: Embed a widget that calls back to vector/text search to suggest next actions as users interact.

3. Solution Architecture

We deployed a scalable, secure cloud infrastructure:

This diagram shows how each microservice—from parsing to runtime widget—connects to deliver an end-to-end cloud-native agentic workflow.

Key Innovations:

1. Layered Parsing: Combining OCR/LayoutLM with Mistral vision captures both text fidelity and visual context.
2. LanGraph + RAGFlow Integration: Blends parsed content, embeddings, and domain knowledge for smarter schema generation and runtime insights.
3. Cloud-Native Scalability: Kubernetes + GPU autoscaling ensures sub-100 ms p95 at 5 k concurrent users.
4. Seamless No-Code Embedding: The widget auto-adapts to any form, providing contextual recommendations as users type.

This design balances cutting-edge AI vision, agent orchestration, and cloud infrastructure to deliver a standout, production-grade workflow.

4. Implementation Highlights

Key components and integrations:

• Parsing Layer (OCR → LayoutML): Extract text and layout elements by running Tesseract OCR followed by a fine-tuned LayoutLM model, achieving 94% field detection recall on diverse form styles.
• LLM Vision Inference: After structural parsing, we feed the extracted layout JSON and original PDF images into Mistral Document Intelligence to capture nuanced visual cues—boosting accurate field labeling and table recognition by an additional 8%.
• Vectorization & Search: Generate embeddings for parsed text blocks and store them in pgvector, enabling semantic similarity searches alongside keyword indexing.
• Agent Orchestration (LangGraph + RAGFlow): Use LangGraph to sequence tasks and RAGFlow to merge schema, embeddings, and external knowledge—providing context-aware recommendations when forms require business logic lookups.
• Model & Deployment: Llama3-27B hosted on Kubernetes with GPU auto-scaling; quantized 4-bit for efficiency.
• Distributed Tasks (Celery): Manage long-running ingestion, parsing, and inference jobs with Celery queues and Redis backends.
• Dynamic API & UI Injection: FastAPI endpoints register new form schemas at runtime; the no-code builder injects a JavaScript widget calling both vector and text-search backends for real-time guidance.

These highlights show the end-to-end, layered inference pipeline—starting with OCR/LayoutLM, then image-based LLM vision, followed by retrieval and orchestration—to maximize accuracy and flexibility.

5. Performance & Benchmark Data

We measured end-to-end metrics over 500 document templates:

This table provides clear before-and-after comparisons, quantifying speed, accuracy, and reliability gains.

6. Multi-Agent Patterns for Dynamic App & Workflow Automation

The no-code builder supports apps, forms, workflow orchestration, role definitions, and approval flows via its visual interface and REST APIs. To automate end-to-end app creation, we devised a set of specialized agents—each driven by prompt patterns—to configure and invoke platform APIs:

• Form Definition Agent: Takes the JSON schema from our LLM agent and transforms it into the builder’s DSL for form fields, labels, and validation rules.
• Workflow Agent: Reads a natural-language workflow description (e.g., "On submit, validate data then trigger approval") and outputs the platform’s workflow JSON, including task nodes, triggers, and error handlers.
• Role & Permission Agent: Generates role-based access control configurations by mapping organizational roles to form and workflow permissions.
• Approval Flow Agent: Constructs multi-stage approval sequences—e.g., manager review → legal sign-off—by prompting for stage definitions and injecting them into the builder’s approval API.
• API Orchestration Agent: Coordinates API calls for app creation in correct order—registering forms, workflows, roles, and approvals—handling retries and error logic via LangGraph.

By breaking automation into discrete, focused agents, non-technical admins can drive complex app and workflow setups through simple prompts—streamlining everything from form creation to governance.

We measured end-to-end metrics over 500 document templates:

7. Domain-Specific Model Fine Tuning (MVP)

To validate that our agentic pipeline could handle specialized terminology and layouts, we built a fine-tuning MVP for Llama3-27B using LoRA adapters:

1. Data Collection: Curated anonymized domain documents (e.g., 1 000 pharma regulatory forms, 1 000 legal contracts, 1 000 financial tables).
2. Adapter Training: Applied low‑rank adaptation (LoRA) to fine-tune Llama3-27B on combined text + layout token embeddings over 2 epochs, leveraging 4× A100 GPUs in a secure cloud MLOps environment.
3. Evaluation Results: Measured field-extraction accuracy uplift over baseline:Pharma Forms: 94.0 % → 99.2 % (+5.2 pp)Legal Templates: 92.7 % → 97.5 % (+4.8 pp)Financial Tables: 91.3 % → 97.4 % (+6.1 pp)
4. Pharma Forms: 94.0 % → 99.2 % (+5.2 pp)
5. Legal Templates: 92.7 % → 97.5 % (+4.8 pp)
6. Financial Tables: 91.3 % → 97.4 % (+6.1 pp)
7. Production Decision: The MVP confirmed that targeted fine-tuning delivers significant gains; full rollout was postponed pending compliance certification and federated model management design.

This MVP highlights how adaptable LLMs can rapidly gain domain expertise and informs our roadmap for secure, maintainable model updates in production.

6. Lessons Learned & Trade-Offs

• OCR vs. LayoutLM: Pure OCR misaligned tables; adding LayoutLM boosted field recall by 12% at the cost of 200ms extra latency.
• Agent Confidence: LLM schema suggestions occasionally missed edge-case labels—solved by adding a lightweight human-in-loop review for low-confidence fields (<0.6 score).
• Security & Observability: We integrated OpenTelemetry and Prometheus to monitor parse failures and API latencies, ensuring sub-200ms P95.

7. Business Impact & ROI

The agentic solution unlocked significant business value:

1. Time-to-Market: Form rollouts dropped from weeks to minutes, enabling faster client onboarding.
2. Cost Savings: Reduced developer effort saved $200K/year in headcount.
3. User Satisfaction: End-user fill-rate improved by 22% due to accurate, context-aware fields.

The calculator snippet helps readers estimate annual savings by inputting templates and developer rates.

ROI Calculator

8. Where 8tomic Labs can help

Interested in automating your document-to-app workflows?
Book a session with 8tomic Labs to design and deploy your own agentic pipeline—on cloud best-in-class infrastructure, security, and monitoring.

Book your 30-minute AI/Product Consulting Session ↗

Written by Arpan Mukherjee

Founder & CEO @ 8tomic Labs