AI Automation
Automate document-, email- and decision-heavy work by combining AI with rules, integrations and existing automation tools.
- Understand requestIntent: refund · order #4821
- Retrieve policyRefund policy v3 · 2 sources cited
- Call toolsorders.lookup · payments.status
- 4Human approvalRefund above auto-approve limit
- 5Execute actionpayments.refund
- 6Respond & logCustomer reply · audit trail
Tool call
orders.lookup({
order_id: "4821"
}) → { status: "delivered",
amount: 42.00 }Approval required
Refund 42.00 to original payment method
Division
Service area
AI Agents & Intelligent Automation
Team
Engagement
Project · Team · Managed
Overview
Many processes resist traditional automation because inputs are unstructured — emails, PDFs, free-text forms, calls. AI automation adds the missing capability: understanding and classifying that input, extracting data, making bounded decisions and handing results to rule-based automation or people. We combine AI with workflow engines, RPA where it already exists and direct integrations to automate end-to-end, while keeping exceptions visible.
Common use cases
- Email and inbox automationClassify, extract and route inbound emails to the right queue or system.
- Form and application processingValidate and enrich submitted data automatically.
- Back-office reconciliationMatch transactions, invoices and records with AI-assisted exception handling.
- Report generationAssemble recurring reports from multiple systems with AI-written commentary.
Quick answers
AI Automation at a glance
The essentials in brief. Every project is scoped individually — ask us for specifics.
- What is AI automation?
- Automate document-, email- and decision-heavy work by combining AI with rules, integrations and existing automation tools.
- Who is it for?
- Typically product companies adding AI features, enterprises automating knowledge work, and teams whose AI pilot needs to become a dependable production system.
- What does Shivacha provide?
- Process analysis
- Classification & extraction
- Workflow integration
- Confidence routing
- Measurement
- Continuous improvement
- Which technologies are used?
- AI Agents, Large Language Models, OpenAI Models, Retrieval-Augmented Generation, Python, Node.js — chosen to fit your stack and constraints.
- How does the process work?
- Map the workflow → Choose the autonomy level → Build tools and guardrails → Shadow mode → Expand autonomy.
- What affects the cost?
- Number and quality of data sources
- Accuracy targets and evaluation effort
- Integrations with business systems
- Model choice, hosting and per-request cost
- Human-in-the-loop and audit requirements
- Data residency and privacy constraints
- How long does it take?
- A scoped proof of value usually takes 4–8 weeks; a production system with evaluation, integrations and guardrails typically takes 3–6 months.
- How do I get started?
- Share a short brief in the form below, book a 30-minute call or message us on WhatsApp. A senior engineer replies within one business day; NDA on request.
Capabilities
What we deliver
Process analysis
Identify steps where AI removes manual effort and where rules suffice.
Classification & extraction
Models tuned to your document and message types.
Workflow integration
Connections to workflow engines, RPA and business systems.
Confidence routing
Low-confidence items routed to human review automatically.
Measurement
Throughput, accuracy and time-saved tracking.
Continuous improvement
Reviewed exceptions feed back into prompts and models.
Architecture
Engineered right from day one
The layers we typically design for AI agents & intelligent automation, adapted to your stack and partners.
- Least-privilege toolsAgents receive narrowly scoped tools rather than broad system access.
- Deterministic where possibleRules, schemas and code handle what does not need a model; the model handles judgement.
- Full auditabilityEvery tool call, input and output is logged for review and debugging.
- Graceful failureUncertain or failed steps route to an exception queue instead of guessing.
Delivery
How an engagement runs
- 1
Map the workflow
Document the current process, decisions, systems touched, exceptions and who owns each step.
- 2
Choose the autonomy level
Decide which steps are fully automated, which are suggested, and which always need a human.
- 3
Build tools and guardrails
Expose narrow, well-typed tools with scoped permissions and validation.
- 4
Shadow mode
Run the agent alongside humans, compare outcomes and tune before it acts on its own.
- 5
Expand autonomy
Increase automation per task as measured reliability justifies it.
Security
Security built into delivery
Controls we apply by default on this kind of work — not a separate phase at the end.
Data boundaries
Permission-aware retrieval so users only see answers from documents they may access.
Guardrails
Input and output checks, tool allow-lists and human approval for consequential actions.
No training on your data
Provider settings and contracts chosen so your data is not used to train third-party models.
Audit trail
Prompts, sources, tool calls and approvals logged for review.
Technology
Tools we use for this
Products
Start from a platform
Shivacha AI Workflows
Orchestrate processes where rules, integrations, AI and people each do their part.
Learn moreShivacha Agent Platform
Build, govern and operate AI agents that do real work across your systems.
Learn moreShivacha AI Sales Agent
An AI sales assistant that qualifies, researches and books — inside your CRM.
Learn moreRelated services
Often combined with
AI Agent Development
AI agents that plan, use tools and complete multi-step business tasks — with scoped permissions, approvals and full audit trails.
Learn moreAgentic AI Development
Multi-agent and agentic systems that coordinate specialised agents across long-running workflows with supervision and control.
Learn moreAI Workflow Automation
Design and build AI-powered workflows that connect your systems, route decisions and escalate exceptions automatically.
Learn moreDedicated team
AI Agent Team
Specialists in agentic systems, tool integration and workflow automation.
Work & insights
Related thinking
Agentic claims intake with human approval
A reference design for an AI workflow that reads claim submissions, extracts and validates data, and prepares cases for adjusters.
Learn morePermission-aware enterprise knowledge assistant
How we design a RAG assistant that answers from thousands of internal documents while respecting every user's access rights.
Learn moreAI development cost: what you pay for when you build an AI product or agent
Model fees are rarely the main cost. Data preparation, evaluation, integrations and guardrails decide both the budget and whether the system works.
Learn moreFAQ
Frequently asked questions
Does AI automation replace RPA?
It usually complements it. RPA handles deterministic UI steps; AI handles understanding unstructured input. Where APIs exist, direct integration beats both.
What accuracy can we expect?
It depends on the task and data. We measure accuracy on a sample of your real cases during discovery and design confidence thresholds accordingly.
What is the difference between automation and an AI agent?
Traditional automation follows fixed rules. An AI agent can interpret unstructured input, decide which steps to take and adapt to variations — while still being constrained by the tools and policies you give it. Most production systems combine both.
Are agents safe to connect to production systems?
They can be, with scoped credentials, validated tool inputs, approval gates for consequential actions, rate limits and complete action logs. We introduce autonomy gradually, starting in shadow mode.
Next step
Build Your AI Product.
Tell us about your AI automation requirements — goals, timeline and constraints. We will reply with questions, an approach and next steps.
- Senior engineer reads every enquiry
- Reply within one business day
- NDA on request
Your details are used only to reply to this enquiry.