Navigating a sea of paperwork, scattered throughout varied platforms, could be a daunting process, usually resulting in sluggish decision-making and missed insights. As organizational information and information multiplies, groups that may’t centralize or floor the best data rapidly will battle to make choices, innovate, and keep aggressive.
This weblog explores how the brand new Discuss to My Docs (TTMDocs) agent gives an answer to the steep prices of information fragmentation.
The excessive price of information fragmentation
Information fragmentation is not only an inconvenience — it’s a hidden price to productiveness, actively robbing your group of time and perception.
- A survey by Starmind throughout 1,000+ information staff discovered that workers solely faucet into 38% of their out there information/experience as a result of of this fragmentation.
- One other examine by McKinsey & Associates discovered that information staff spend over 1 / 4 of their time trying to find the data they want throughout totally different platforms comparable to Google Drive, Field, or native techniques.
The constraints of current options
Whereas there are a number of choices in the marketplace designed to ease the method of querying throughout key paperwork and supplies residing in a wide range of locations, many have important constraints in what they will really ship.
For instance:
- Vendor lock-in can severely hinder the promised expertise. Except you might be strictly utilizing the supported integrations of your vendor of alternative, which in most cases is unrealistic, you find yourself with a restricted subset of data repositories you may hook up with and work together with.
- Safety and compliance issues add one other layer of complexity. When you’ve got entry to 1 platform or paperwork, chances are you’ll not want entry to a different, and any misstep or missed vulnerability can open up your group to potential danger.
Discuss to My Docs takes a special strategy
DataRobot’s new Discuss to My Docs agent represents a special strategy. We offer the developer instruments and help you could construct AI options that really work in enterprise contexts. Not as a vendor-controlled service, however as a customizable open-source template you may tailor to your wants.
The differentiation isn’t refined. With TTMDocs you get:
- Enterprise safety and compliance inbuilt from day one
- Multi-source connectivity as a substitute of vendor lock-in
- Zero-trust entry management (Respects Present Permissions)
- Full observability by way of DataRobot platform integration
- Multi-agent structure that scales with complexity
- Full code entry and customizability as a substitute of black field APIs
- Trendy infrastructure-as-code for repeatable deployments
What makes Discuss to My Docs totally different
Discuss To My Docs is an open-source utility template that provides you the intuitive, acquainted chat-style expertise that trendy information staff have come to anticipate, coupled with the management and customizability you really need.
This isn’t a SaaS product you subscribe to; however somewhat a developer-friendly template you may deploy, modify, and make your individual.
Multi-source integration and actual safety
TTMDocs connects to Google Drive, Field, and your native filesystems out of the field, with Sharepoint and JIRA integrations coming quickly.
- Protect current controls: We offer out-of-the-box OAuth integration to deal with authentication securely by way of current credentials. You’re not making a parallel permission construction to handle—should you don’t have permission to see a doc in Google Drive, you received’t see it in TTMDocs both.
- Meet information the place it lives: In contrast to vendor-locked options, you’re not pressured emigrate your doc ecosystem. You may seamlessly leverage information saved in structured and unstructured connectors like Google Drive, Field, Confluence, Sharepoint out there on the DataRobot platform or add your information domestically.
Multi-agent structure that scales
TTMDocs makes use of CrewAI for multi-agent orchestration, so you may have specialised brokers dealing with totally different features of a question.
- Modular & versatile: The modular structure means you can even swap in your most well-liked agentic framework, whether or not that’s LangGraph, LlamaIndex, or every other, if it higher fits your wants.
- Customizable: Wish to change how brokers interpret queries? Alter the prompts. Want customized instruments for domain-specific duties? Add them. Have compliance necessities? Construct these guardrails straight into the code.
- Scalable: As your doc assortment grows and use circumstances turn out to be extra complicated, you may add brokers with specialised instruments and prompts somewhat than attempting to make one agent do all the things. For instance, one agent would possibly retrieve monetary paperwork, one other deal with technical specs, and a 3rd synthesize cross-functional insights.
Enterprise platform integration
One other key side of Discuss to my Docs is that it integrates along with your current DataRobot infrastructure.
- Guarded RAG & LLM entry: The template features a Guarded RAG LLM Mannequin for managed doc retrieval and LLM Gateway integration for entry to 80+ open and closed-source LLMs.
- Full observability: Each question is logged. Each retrieval is tracked. Each error is captured. This implies you’ve full tracing and observability by way of the DataRobot platform, permitting you to truly troubleshoot when one thing goes flawed.
Trendy, modular elements
The template is organized into clear, impartial items that may be developed and deployed individually or as a part of the complete stack:
| Element | Description |
| agent_retrieval_agent | Multi-agent orchestration utilizing CrewAI. Core agent logic and question routing. |
|
core |
Shared Python logic, widespread utilities, and features. |
| frontend_web | React and Vite internet frontend for the consumer interface. |
| internet | FastAPI backend. Manages API endpoints, authentication, and communication. |
| infra | Pulumi infrastructure-as-code for provisioning cloud assets. |
The ability of specialization: Discuss to My Docs use circumstances
The sample is productionized specialised brokers, working collectively throughout your current doc sources, with safety and observability inbuilt.
Listed below are a number of examples of how that is utilized within the enterprise:
- M&A due diligence: Cross-reference monetary statements (Field), authorized contracts (Google Drive), and technical documentation (native information). The permission construction ensures solely the deal group sees delicate supplies.
- Scientific trial documentation: Confirm trial protocols align with regulatory tips throughout a whole bunch of paperwork, flagging inconsistencies earlier than submission.
- Authorized discovery: Search throughout years of emails, contracts, and memos scattered throughout platforms, figuring out related and privileged supplies whereas respecting strict entry controls.
- Product launch readiness: Confirm advertising and marketing supplies, regulatory approvals, and provide chain documentation are aligned throughout areas and backed by certifications.
- Insurance coverage claims investigation: Pull coverage paperwork, adjuster notes, and third-party assessments to cross-reference protection phrases and flag potential fraud indicators.
- Analysis grant compliance: Cross-reference finances paperwork, buy orders, and grant agreements to flag potential compliance points earlier than audits.
Use case: Scientific trial documentation
The problem
A biotech firm getting ready an FDA submission is drowning in documentation unfold throughout a number of techniques: FDA steering in Google Drive, trial protocols in SharePoint, lab studies in Field, and high quality procedures domestically. The core drawback is guaranteeing consistency throughout all paperwork (protocols, security, high quality) earlier than a submission or inspection, which calls for a fast, unified view.
How TTMDocs helps
The corporate deploys a custom-made healthcare regulatory agent, a unified system that may reply complicated compliance questions throughout all doc sources.
Regulatory agent:
Identifies relevant FDA submission necessities for the precise drug candidate.
Scientific evaluation agent:
Evaluations trial protocols towards business requirements for affected person security and analysis ethics.
Security compliance agent:
Checks that security monitoring and antagonistic occasion reporting procedures meet FDA timelines.
The end result
A regulatory group member asks: “What do we’d like for our submission, and are our security monitoring procedures as much as commonplace?”
As an alternative of spending days gathering paperwork and cross-referencing necessities, they get a structured response inside minutes. The system identifies their submission pathway, flags three high-priority gaps of their security procedures, notes two points with their high quality documentation, and gives a prioritized motion plan with particular timelines.
The place to look: The code that makes it occur
The easiest way to grasp TTMDocs is to take a look at the precise code. The repository is totally open supply and out there on Github.
Listed below are the important thing locations to begin exploring:
- Agent structure (agent_retrieval_agent/custom_model/agent.py): See how CrewAI coordinates totally different brokers, how prompts are structured, and the place you may inject customized habits.
- Software integration (agent_retrieval_agent/custom_model/software.py): Exhibits how brokers work together with exterior techniques. That is the place you’d add customized instruments for querying an inner API or processing domain-specific file codecs.
- OAuth and safety (internet/app/auth/oauth.py): See precisely how authentication works with Google Drive and Field and the way your consumer permissions are preserved all through the system.
- Net backend (internet/app/): The FastAPI utility that ties all the things collectively. You’ll see how the frontend communicates with brokers, and the way conversations are managed.
The way forward for enterprise AI is open
Enterprise AI is at an inflection level. The hole between what end-user AI instruments can do and what enterprises really need is rising. Your organization is realizing that “ok” shopper AI merchandise create extra issues than they remedy once you can not compromise on enterprise necessities like safety, compliance, and integration.
The longer term isn’t about selecting between comfort and management. It’s about having each. Discuss to my Docs places each the ability and the pliability into your arms, delivering outcomes you may belief.
The code is yours. The probabilities are infinite.
Expertise the distinction. Begin constructing as we speak.
With DataRobot utility templates, you’re by no means locked into inflexible black-box techniques. Achieve a versatile basis that allows you to adapt, experiment, and innovate in your phrases. Whether or not refining current workflows or creating new AI-powered functions, DataRobot provides you the readability and confidence to maneuver ahead.
Begin exploring what’s doable with a free 14-day trial.
