Companies face many different options for processing documents today. Some are still using older legacy technology based on Optical Character Recognition (OCR) or Robotic Process Automation (RPA), and supplementing these solutions with Business Process Outsourcing (BPO), or manual data cleanup and data entry, while others are progressing into AI-native approaches.
Due to the rapid changing landscape of AI solutions, the intrinsic complexity and unique business processes of each organization, there is a classic technology strategy question that is emerging, similar to past waves of technical and process innovation:
Is it better to build your own solution, tailored to your business processes, internal architecture, and technology development skillset from basic individual component parts, or buy a best-of-breed platform which can offer more purpose-built capabilities, higher automation and accuracy output, and faster time to value out of the box?
Selecting the Right Path
At Hyperscience we regularly help our customers and prospects assess the right path for their needs. We often find our customers have deployed a hybrid solution blending the two approaches together as well. Technology investments and development are not easy decisions to make, and many different factors come into play.
Two of the most important factors are time to deploy the solution and cost involved, but calculating this cost is no easy task. When organizations deploy technology at scale, the cost of technical staffing, infrastructure, application development, and ongoing maintenance all play an important role.
For this reason, we have created a Build versus Buy Whitepaper, which compares the two options:
- Build a custom platform leveraging tools and services from a hyperscaler (such as AWS, Google Cloud Platform, or Azure).
- Buy a customizable ML-native document processing platform, such as Hyperscience Hypercell.
The central tool for this comparison is a 5-year Total Cost of Ownership (TCO) model, contrasting the value of Hyperscience’s unified, out-of-the-box platform against the build-it-yourself approach required by hyperscaler offerings.
To do this, we have built a model centered on various key assumptions:
Document Volume:
1M pages / year
Deployment mode:
Hyperscaler-built IDP solution v. Hyperscience Hypercell on a hyperscaler SaaS architecture
Use Case Complexity:
Medium complexity use case for end-to-end Document Ingestion, Classification, Entity Extraction, Document Enrichment, Results Validation
Technical Labor Assumptions:
Costs for technical labor staff including Infrastructure Engineers, Machine Learning Model Engineers, Security Engineers, Project Managers
Timeframe:
Initial setup to get the system implemented and functional
Ongoing Maintenance (1-5 years post go-live)
Exceptions:
No Full Human-in-the-Loop
No Full Model Lifecycle Management
No Accuracy Harnessing
Discount Rate for Net Present Value (NPV) calculation:
10%
Key Finding: The Staggering Difference in Costs
The TCO Model differentiates between “Dark Green Costs” (hard costs easily measurable on spreadsheets) and “Light Green Benefits” (business benefits harder to quantify but compounding over time).
1. The Dark Green Costs (PV Total Cost over 5 Years):
The costs associated with building an IDP solution in-house using hyperscaler services are overwhelmingly driven by technical labor and ongoing maintenance.
Approach 1: Build (Hyperscaler DIY)
NPV Total cost over 5 years: $2,275,442
Key Cost Drivers:
Technical labor is the largest component at $1,866,891 PV. This includes staffing for 2 full-time Infrastructure Engineers and 1 full-time Model Engineer in the initial year, plus ongoing staffing for Infrastructure, Model, Security, and Project Management in subsequent years. Technical infrastructure costs (AI API services, monitoring, deployment pipelines) total $408,552 PV.
Approach 2: Buy (Hyperscience Hypercell)
NPV Total cost over 5 years: $682,413
Key Cost Drivers:
This cost includes a one-time initial implementation cost ($100,000 PV) for model training and workflow design, and recurring annual platform fees ($153,639 per year) for the Hypercell platform.
By selecting Hyperscience instead of undertaking a DIY build, organizations can realize a cost savings benefit of $1,593,029 over five years.
2. The Performance Benefits (Light Green):
Cost avoidance is only one dimension of ROI. The model also quantified measurable performance benefits (“Light Green Benefits”) achieved by using the specialized Hyperscience platform, totaling $263,687 PV over five years. These benefits include:
- Reduced training time and effort due to the Hyperscience ORCA (Optical Reasoning and Cognition Agent) Vision Language Model (VLM) framework, which offers a zero-shot experience, eliminating the need for training with sample documents for certain use cases.
- Reduced cost of manual document processing through sophisticated Human-in-the-Loop (HITL) thresholding and accuracy harnessing.
- Increased accuracy (on average 10%+ higher accuracy level versus hyperscaler IDP solutions).
- Least cost routing for model execution, optimizing resource utilization over time.
The Bottom Line: ROI and Time to Value
By combining the significant cost savings ($1,593,029) and the improved performance benefits ($263,687), the total Net Benefits realized by choosing Hyperscience amount to $1,856,716 PV.
Our financial analysis concluded that organizations experience a 272% ROI over five years, with an estimated payback period of less than 6 months.
Why the DIY Approach Stalls: Complexity and Time
Organizations often mistakenly use the initial short-term price-per-page calculation to anchor their IDP decisions, overlooking the engineering hours, infrastructure scaling, and ongoing model maintenance that drive the true Total Cost of Ownership.
- Time to Value: Companies attempting to build their own IDP platforms with hyperscaler tools often face 12–18 months of development before seeing measurable benefits, not including the ongoing maintenance burden. By contrast, Hyperscience customers routinely achieve production-ready deployments in a fraction of that time.
- Limitations in Complexity: While hyperscalers may be a fit for simple, standardized scenarios (like single government forms), they quickly run into limitations—in both accuracy and cost—once documents involve variability, exceptions, or downstream dependencies. Reaching high levels of operational effectiveness (90% or higher) requires advanced capabilities like model lifecycle management and sophisticated HITL controls, which are resource-intensive to build from scratch. Hyperscience customers routinely achieve 99.5% accuracy and 98% automation.
Technical Edge: The Hyperscience Agentic Approach
Hyperscience employs a unique, agentic extraction approach. Instead of relying on a single model, a goal-oriented agent orchestrates an ensemble of specialized models (including proprietary models, Vision Language Models, and LLMs) across the document processing pipeline. This system is dynamic and flexible, allowing the agent to prioritize accuracy, speed, cost, or compliance thresholds based on the desired business outcome.
Crucially, the optimal path for most organizations is not Hyperscience versus hyperscalers, but Hyperscience running on hyperscaler infrastructure. Deploying the Hypercell platform within an existing AWS, Azure, or GCP environment provides the “best of both worlds,” combining the scale and flexibility of the cloud with the accuracy, automation, and essential capabilities like model drift management—a capability hyperscalers do not natively offer.
Ultimately, embracing a TCO perspective ensures that the technology investment translates into measurable business value, positioning the organization for long-term growth and success.
To learn more about the costs and benefits of implementing Hyperscience Hypercell, and modelling out what your organization’s document processing strategy could look like, download the full Build v. Buy whitepaper here.