The proof-of-concept phase for Document AI is over. Indian enterprises that spent 2023 and 2024 experimenting with OCR and large language model combinations for document processing are now facing a harder question: how do you run this in production without breaking the bank or creating a maintenance nightmare?
The answer is forcing CIOs and CTOs to make architectural bets that will be difficult to reverse. Choose wrong, and you’re looking at brittle integrations, runaway costs, and compliance gaps that auditors will flag within quarters.
The Production Reality Check
Document-heavy workflows — contract analysis, invoice processing, KYC verification — are where AI delivers measurable ROI. A well-tuned pipeline can cut processing time from days to minutes. But the gap between a working demo and a production system handling thousands of documents daily is vast.
Production systems need to handle throughput spikes, maintain consistent latency, support model retraining without downtime, and produce audit trails that satisfy regulators. Most prototype architectures collapse under these requirements.
This is why the industry is converging on microservice pipelines — breaking document processing into discrete, independently scalable components. One service handles ingestion, another runs OCR (optical character recognition, which converts images to text), a third applies LLM-based extraction, and so on. Each piece can be monitored, updated, and scaled separately.
The Build vs Buy Trade-off
Google, Microsoft, and AWS all offer managed Document AI services. Google’s Document AI platform, Azure AI Document Intelligence, and Amazon Textract provide pre-built capabilities that can get you to production faster. Infosys and other system integrators are building practices around deploying and customising these platforms for enterprise clients.
The appeal is obvious: faster time to value, less infrastructure to manage, and access to models trained on massive datasets. For straightforward use cases like standard invoice processing, managed services often make sense.
But managed services come with constraints. You’re tied to the vendor’s model update schedule, their pricing structure, and their data residency options. When a hyperscaler decides to deprecate an API or change pricing tiers — which happens regularly — you’re along for the ride.
Building your own microservice orchestration layer gives you control. You can swap out OCR engines, fine-tune LLMs on your specific document types, and keep sensitive data entirely within your infrastructure. The cost is complexity: you need teams who can build and maintain cloud-native systems with proper observability.
What Indian Enterprises Should Watch For
Data residency is non-negotiable for many Indian organisations, particularly in financial services and healthcare. Before committing to any managed service, verify exactly where your documents are processed and stored. Some services route data through regions that may not satisfy your compliance requirements.
Observability — the ability to see what’s happening inside your system — is often an afterthought in managed services. When a document fails to process correctly, can you trace exactly where and why? For regulated industries, this isn’t optional.
Cost modelling for Document AI is notoriously tricky. Managed services charge per page or per API call, which seems straightforward until you factor in retries, reprocessing for quality issues, and the hidden costs of data transfer. Run realistic projections based on your actual document volumes, not vendor-provided estimates.
The Composable Middle Path
Smart architecture teams are finding a middle ground: using managed services for commodity tasks like basic OCR while building custom microservices for business-specific extraction and validation logic. This composable approach limits vendor lock-in to components that are easier to replace.
The key is designing clean interfaces between services from the start. If your LLM extraction layer expects a specific input format, you can swap the upstream OCR provider without rewriting downstream code. This requires upfront architectural discipline but pays off when — not if — you need to change vendors.
Container orchestration platforms like Kubernetes have become the standard for running these pipelines. They provide the scaling, health monitoring, and deployment automation that production systems require. If your team lacks Kubernetes expertise, that’s a gap worth closing before committing to a custom architecture.
What This Means For You
If you’re running document-heavy operations, 2025 is the year to move beyond pilots. Start by auditing your current document workflows and identifying where AI-driven automation would deliver the clearest ROI — typically high-volume, repetitive processes with structured outputs.
For most organisations, the right approach is starting with managed services for speed, but architecting for portability from day one. Insist on clean service boundaries, comprehensive logging, and contracts that allow data export. Your future self will thank you when the inevitable vendor renegotiation arrives.
The enterprises that get this right will process documents faster and cheaper than competitors. Those that don’t will spend the next three years untangling technical debt. The architecture decisions you make in the next six months will determine which camp you’re in.