Problem:
High-volume Processes
Case:
Company loss:TIME
Autonomous Systemsfor Businesses
Modern applications to streamline and automate processes:
Document-heavy tasks | Admin | Back-office | Customer support
Insurance BrokersManufacturingReal EstateAccountancy
Towards Autonomous Enterprise
Document Workflowprocessing
Emailsinbox · attachments
Portalsuploads · forms
PDFs & docsquotes · policies
SpecsPDF · Excel · Docx
JakeAI office manager24/7 Call Centerlive call
Inbound calls
JemmaAI call supportCallerCan you move my delivery to Thursday morning?
JemmaSure. Rescheduled to Thu, 9-12. Confirmation texted.
CallerFantastic, thanks!
- Clear business value and ROI
- Human review preserved
- No software migration
- Grounded in domain expertise, processes and corporate data
Signals for Autonomous Systems
For Industries:
Manufacturing
Commercial Insurance Brokerage
Real Estate
Retail
Clinics
Accountancy
Integration approach
Platform-Agnostic Integration
DocBeaver provides professional development and consulting services across your company's software, tools, and components. We build AI agents and automations on top of existing tools, so no software migrations are needed. Workflows remain backward compatible and can be disconnected from our AI agents and automations at any moment. The best choice is defined by audit discoveries, proven automation practices, and client preferences.
Targets
Workflow-specific
Published solution pages model expected reductions by workflow, with assumptions separated for intake, checking, reconciliation and evidence review.
Review
Human review
Low-confidence extraction, conflicting source evidence and consequential outputs stop at review gates before system update or client-facing release.
Infrastructure
No migrations
Implementations are designed around current document stores, email, spreadsheets, CRM, ERP and practice-specific tools.
Methodology and Deliverables
To provide best results we use widely acknowledged and battle-tested combination of automation and agents. Our agents are task-specific, narrow-scoped and never act freely on their own. We incorporate them in automation workflows, so they sit tight and act safely within strict permissions, fully observable.
Most of the time, programmatic automation handles 75-85% of the work. Agents do the rest 15-25%. This combination gives the best efficiency, as it leverages automations' stability and deterministic agents reasoning.
To understand conceptually, how and when use automations versus agents, read this article.
Workflow audit
Document inventory, source-system map, baseline effort estimate, exception list, approval points and first-workflow recommendation.
Prototype on real documents
Small working prototype, platform-vs-custom decision, sample outputs, confidence thresholds and early failure cases.
Review and control design
Human review rules, evidence display, reviewer actions, audit trail requirements and release criteria for system updates.
Implementation
Production workflow, integrations, extraction and validation logic, exception queues, logs and operational monitoring.
Testing and rollout
Test set from real documents, before-and-after measurements, staff feedback, tuning backlog and staged deployment plan.
Proof layer
Case examples and measurable targets
DocBeaver scopes document automation like a service page, not a generic tool demo: each candidate workflow needs a named industry, a specific operational problem, a controlled approach and a metric to test before broad rollout.
Client file control across BMS, Microsoft 365 and portals
Challenge: A 50-person broking team loses time reconstructing client evidence across BMS records, Outlook, Teams, SharePoint, portal outputs, PDFs and spreadsheets.
Approach: Controlled file-control workflow with AI classification, client-policy matching, exception queues and human approval for uncertain or high-risk evidence.
Target: 20 minutes saved per user per day, 3.6-month payback model
Read case modelRFQ, BOM, product data and quality evidence workflow control
Challenge: Manufacturing teams handle enquiries, drawings, BOMs, configurations, supplier documents, product data, NCR evidence and dispatch packs through inboxes and disconnected production systems.
Approach: Document intake, revision checks, PO-versus-quote or PO-versus-configuration validation, supplier evidence tracking and review gates before ERP, MRP or QMS updates.
Typical target: 20-40% faster RFQ preparation, 40-75% faster dispatch or evidence packs
Read manufacturing case modelOur Implementation Process
Audit
Map document types, manual steps, risk points, outputs, systems, and approval moments before custom AI agent development.
Prototype
Test the recommended platform, AI integration, and custom-code mix on real documents before building the full workflow.
Review design
Define where human approval is needed, what reviewers see, and how exceptions are resolved.
Implementation
Build custom AI agents for intake, extraction, validation, output generation, integrations, and logging.
Testing with real documents
Measure the workflow against edge cases, missing fields, staff corrections, and final output quality.
Deployment and improvement
Launch the process, monitor failures, and improve AI agent rules, prompts, and integrations over time.
Audit your company on Autonomy feasibility
The audit is a focused discovery step and initial audit conversation is free. It produces a practical view of the smallest reliable workflow to prototype, what should remain under human review, and what must connect to existing systems before any paid implementation scope is proposed.
Format30-minute scoping call followed by a focused workflow assessment when the fit is clear
CostFree for the initial audit conversation; implementation is scoped separately
OutputA written recommendation covering the first workflow, risks, systems, data needs and success metrics
Next stepPrototype scope, implementation estimate and rollout path only if the workflow is a strong candidate
What we map
- Document types, sources, volumes and formats
- Manual decisions, approval gates and exception paths
- Target outputs, destination systems and integration boundaries
- Automation candidates, risks, review rules and prototype scope
What you get back
A recommended first workflow, build-vs-platform notes, review rules, integration assumptions, data and document requirements, and the success metrics to test before a wider deployment.
FAQ
What does DocBeaver do?
DocBeaver provides AI agents and AI automations for document-heavy companies. The team designs and builds controlled workflows that combine automation, document AI, narrow AI agents, integrations, human review and traceability.
Which document AI platforms can DocBeaver work with?
DocBeaver is platform-agnostic and can combine tools such as ABBYY, Rossum, Azure Document Intelligence, Google Document AI, Nanonets, UiPath, Power Automate, n8n, Python, OpenAI, Claude, and custom components as part of AI integration and custom AI agent development projects.
Why start with an AI document automation audit?
The audit maps document types, manual decisions, systems, outputs, quality risks, approval gates, tool boundaries, and evaluation criteria before recommending the smallest dependable AI agent or automation workflow to prototype or build.
How long does AI document automation implementation take?
A focused prototype can often be scoped after the audit and tested on real documents first. A contained production AI agent or automation workflow is usually planned in stages, with timing depending on document variety, system access, approval rules, integration depth and testing requirements.
What does the audit produce?
The audit produces a practical workflow recommendation: document and source-system map, automation candidates, human review points, integration assumptions, risk notes, data requirements, prototype scope and success metrics.
How much does the audit cost?
The initial audit conversation is free. If the workflow is a strong fit, DocBeaver then scopes any paid prototype or implementation separately with deliverables, assumptions and commercial terms agreed before work starts.
Where is human review used?
Human review is used where confidence is low, source documents conflict, regulated or financial outputs need approval, external messages may be sent, or staff need traceability before results reach Word, Excel, CRM, SharePoint, Drive, or databases.
Contact
Or call us:
0333 0540 233