Your Enterprise Runs on Documents. Most of That Knowledge Stays Dark.

Most failed AI pilots share the same root cause: they were custom-built experiments, not configurable deployments. Teams underestimated the complexity of document ingestion pipelines, integration with existing systems, and the operational overhead of running AI in production. What ThirdEye Data delivers is not a pilot; it is a pre-built system that has already cleared those hurdles in production for other clients. The Week 1-2 discovery phase is specifically designed to surface and resolve the three issues that kill AI projects: data readiness gaps, integration blockers, and access control complexity. You see a working system on real documents by week 6, not a prototype that requires 12 more months of engineering to become production-ready.

The business case for this suite is built on four hard numbers, all calculable from your own operational data before you sign anything.

• First, labor cost recovery: take your average document/information processing time per file type, multiply by daily volume, and price it at your blended staff cost; this is your addressable savings pool.
• Second, error and rework cost: what does a mis-keyed invoice, a missed contract clause, or an incorrect audit record actually cost you in rework, late payments, or compliance penalties?
• Third, speed-to-decision value: how much does a 3-week contract review delay cost in deferred revenue or missed deadlines?
• Fourth, compliance overhead reduction: what does your team spend on audit evidence assembly, regulatory reporting prep, and compliance document management?

We walk through this model in the first call with your actual numbers and produce a conservative ROI estimate with a payback period, before you commit to anything.

The minimum entry point is a single-module deployment; typically Optira for document automation or AI Search for knowledge retrieval.

Each module is scoped as a fixed, standalone engagement: defined document types, defined integrations, defined outcome benchmarks, deployed in 6 weeks.

The investment starts in a range accessible to mid-market organizations. You do not need to commit to the full suite upfront. The architecture is designed to add modules over time without re-platforming, so the first deployment is both a standalone value delivery and a foundation for expansion.

Our recommendation for most organizations: prove one module in one department, measure the ROI in 90 days, and use that internal proof point to justify the next module.

“First module live in 6 weeks” means a working production system configured against your actual document types, connected to your document sources, and processing real documents, not a demo environment or a sandbox with sample data.

It includes: configuration of the module against your document types, integration with your nominated document source (SharePoint, S3, email, or similar), access control integration with your identity provider, validation and accuracy testing on a sample of your real documents, and a handover session with your team.

It does not include: document types not scoped in the initial engagement, integrations with systems not identified in discovery, or custom feature development.

Scope is fixed, which is how we commit to the timeline with confidence.

No. This is one of the most common misconceptions about enterprise AI deployments, and it is what causes organizations to delay starting. Optira and AI Search are designed to ingest from multiple sources simultaneously, including SharePoint, OneDrive, Azure Blob, S3, email servers, network file shares, and processed content from other systems.

You do not need to consolidate your document infrastructure first. What you do need is access credentials and permissions to connect those sources.

During the Week 1-2 discovery phase, we map your document sources, assess their data quality, and determine which ones to connect in the initial deployment. We typically recommend starting with the one or two sources where your highest-value documents live and expanding from there.

It complements them, it does not replace them.

• SharePoint is a document storage and collaboration tool; it is not a document intelligence or extraction engine.
• ServiceNow is a workflow and ticketing platform; it is not a knowledge retrieval or autonomous resolution system.
• Teams is a communication tool.

This suite sits as an intelligence layer on top of your existing investments:

• Optira ingests from SharePoint and extracts structured data from documents stored there.
• AI Search indexes your SharePoint and Confluence content and makes it queryable in natural language.

Your existing platforms do not change; they become more intelligent and value-driven because this suite reads, understands, and acts on the content within them.

Accountability is designed into the architecture, not assigned after the fact. Every document extraction has a confidence score. Anything below your configured threshold (typically 90-95%) is not passed downstream automatically. It is surfaced to a human reviewer with the AI’s best answer pre-populated, the source document highlighted, and the specific field flagged. The human confirms or corrects. Every decision, AI output, human validation, and final disposition is logged in an immutable audit trail with timestamps and user identity. This means you can always answer “who approved this output and when.”

For regulated industries, this audit trail is what satisfies HIPAA, SOX, and ISO auditors. Contractually, ThirdEye Data provides standard professional services agreements with a clear scope of work; liability terms are discussed during commercial negotiation and are consistent with enterprise software industry norms.

The deployment is designed to minimize IT burden. In the Week 1-2 discovery phase, we need your IT team for three things:

• Providing access credentials to document sources
• Sharing your identity provider configuration (Active Directory, Okta, or similar) for access control integration
• Confirming your network and cloud environment specifications for deployment architecture.

After that initial collaboration, our engineers handle the configuration, integration, and deployment. Your IT team is not expected to do development work, manage infrastructure, or debug integration issues, that is what you are paying for.

During the 90-day hypercare period, we handle all tuning and adjustments. Ongoing managed service options mean your IT team’s involvement post-deployment is limited to access management and occasional integration updates when your underlying systems change.

The ROI timeline depends on which module you start with and what your baseline looks like, but here is what typical deployments show.

• With Optira (document automation): measurable processing time reduction from week 6, when the first document corpus goes live. By week 12, with your team fully using the system, you can calculate actual hours recovered per week and apply your staff cost to get a dollar figure. Labor savings are typically demonstrable within the first quarter of full operation.
• With AI Search: ticket deflection and query containment metrics are trackable from day one of deployment, your support team sees fewer repetitive questions within the first 30 days.

We include an ROI measurement framework in every deployment so you have the metrics structure to report to leadership, not just usage dashboards.

Legacy document quality is almost always worse than teams expect, and it is one of the most common reasons AI deployments underperform.

Here is how we handle it.

• During the Week 1-2 discovery phase, we assess a sample of your actual legacy documents, not a best-case subset, and give you an honest accuracy estimate for each document type before deployment begins.
• For documents with degraded scan quality, mixed fonts, or inconsistent structure, Optira applies multiple extraction layers and flags low-confidence outputs for human review rather than passing them through at reduced accuracy.
• For document types where quality is too poor for reliable extraction, we tell you that upfront and recommend whether remediation (re-scanning, OCR pre-processing) is worth the effort relative to the volume and value of those documents.

You will not discover a quality problem six months into deployment, we surface it in discovery.

Adoption failure is the silent killer of enterprise AI deployments, and it is rarely caused by the technology. It is caused by unclear value communication to end users, inadequate training, and no internal champion driving usage.

ThirdEye Data’s deployment engagement includes a structured adoption component: a user onboarding session with each functional team using the system, role-specific documentation showing exactly how each team’s workflow changes, and a 30-day usage review where we identify adoption gaps and address them. 

The 90-day hypercare period exists precisely because adoption, not technical functionality, is where deployments succeed or fail.

Your data is always yours. All documents processed by Optira, all extracted structured data, all knowledge graph content, and all audit logs are stored in your environment, not in our infrastructure. If you decide to move to a different system at any point, you retain all your data and all the structured outputs generated during the deployment. The knowledge graph is exportable in standard formats. There is no proprietary data format that creates lock-in at the data layer.

At the application layer, we use open standards for integrations (REST APIs, standard connectors) so reintegration with a replacement system does not require rework of your source systems.

We are confident enough in the value of the system that we do not build lock-in into the architecture.

One word answer is Unambiguous: you own your data.

ThirdEye Data does not use your documents, extracted data, or query history to train our models or any third-party models, unless you explicitly contract for that as a separate service.

Your data does not leave your environment in on-premise or VPC deployments. So the question of third-party model training is moot at the architecture level.

For cloud-assisted deployments, we provide a Data Processing Agreement (DPA) as standard that specifies data handling, retention, deletion rights, and model training restrictions.

On IP: the configurations, integration work, and custom document type models built specifically for your environment during the engagement are yours; they are not repurposed for other clients.

We are happy to have our legal team engage with yours during procurement to answer specific DPA and MSA questions.