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Shivacha — Simplifying Tech Solutions
Shivacha AI

AI systems engineered for real work, not demos.

We design, build and operate generative AI, agentic systems and machine learning that plug into your data, your tools and your controls — and keep working after launch.

Request Technical Proposal20 services
Agent run · Support refund
Illustrative
Customer: My order arrived damaged — can I get a refund?
  1. Understand requestIntent: refund · order #4821
  2. Retrieve policyRefund policy v3 · 2 sources cited
  3. Call toolsorders.lookup · payments.status
  4. 4Human approvalRefund above auto-approve limit
  5. 5Execute actionpayments.refund
  6. 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

Overview

Most organisations have already run an AI pilot. Far fewer have an AI system in production that people trust, that is measured, and that improves over time. The gap is rarely the model. It is retrieval quality, integration with systems of record, evaluation, access control, latency and cost — the engineering around the model.

Shivacha AI is built around that engineering. We treat an AI capability as a software product with a data layer, an orchestration layer, a model layer and an operations layer. Each is designed deliberately: which documents an assistant may see, which tools an agent may call, how outputs are checked, how failures are caught and how a human stays in the loop where the stakes require it.

Services
20
Service areas
4
Ready products
9
Reference architectures
2

Flagship offerings

What Shivacha AI launches.

  • AI Agents
  • Multi-Agent Systems
  • Enterprise AI
  • AI Automation
  • AI + FinTech
  • AI + Web3
  • Risk Intelligence
  • Fraud Detection
  • Decision Systems

Typical implementation

White-label AI platform
3–4 weeks
AI agent platform
3–5 weeks
Business automation system
2–4 weeks
Enterprise AI MVP
3–5 weeks

Timelines refer to software implementation and deployment scope for a defined configuration. Third-party integrations, regulatory approvals, banking and card-issuer onboarding, custody and liquidity agreements, security audits and other external dependencies may require additional time.

White-label platforms

Ready-to-launch foundations.

All products
White-Label AI Platform · Assistant

Sources

Help centre · Policies

Grounding

Citations on

Handoff

Enabled

How do I change my card limit?
Open Cards → Controls and set a new daily limit. Changes apply immediately.Source: Cards help · Section 3
Ask a question…
Illustrative interface · sample data
  • White-label
  • Ready to launch

White-Label AI Platform

The foundations of an AI SaaS product — ready to build your differentiation on.

  • Multi-tenant
  • Model routing
  • Billing
  • Knowledge base
  • Admin
Typical implementation
3–4 weeks
Customization
Highly customizable
Explore AI Platform Request Demo
AI Agent Platform · Agent runs

Run

Claims intake

Steps

Classify → Extract → Check

Approval

Required

StepToolResult
1 · Classifydoc.classifyClaim form
2 · Extractdoc.extract12 fields
3 · Policy checkpolicy.lookupNeeds review
Illustrative interface · sample data
  • White-label
  • Ready to launch

AI Agent Platform

Build, govern and operate AI agents that do real work across your systems.

  • Agent builder
  • Tools
  • Approvals
  • Evaluations
  • Audit log
Typical implementation
3–5 weeks
Customization
Highly customizable
Explore AI Agent Platform Request Demo
AI Customer Operations · Assistant

Sources

Help centre · Policies

Grounding

Citations on

Handoff

Enabled

How do I change my card limit?
Open Cards → Controls and set a new daily limit. Changes apply immediately.Source: Cards help · Section 3
Ask a question…
Illustrative interface · sample data
  • White-label
  • Ready to launch

AI Customer Operations

AI support that resolves, not just responds — grounded in your knowledge and systems.

  • Grounded answers
  • Handoff
  • Ticketing
  • Analytics
Typical implementation
3–4 weeks
Customization
Configurable
Explore AI Customer Operations Request Demo
AI Automation · Agent runs

Run

Claims intake

Steps

Classify → Extract → Check

Approval

Required

StepToolResult
1 · Classifydoc.classifyClaim form
2 · Extractdoc.extract12 fields
3 · Policy checkpolicy.lookupNeeds review
Illustrative interface · sample data
  • White-label
  • Ready to launch

AI Automation

Orchestrate processes where rules, integrations, AI and people each do their part.

  • Workflow builder
  • Document AI
  • Approvals
  • Integrations
Typical implementation
2–4 weeks
Customization
Highly customizable
Explore AI Automation Request Demo

Problems we solve

Where AI initiatives usually break down.

Pilots that never reach production

Prototypes built on notebooks and prompts break down when they meet real data volumes, permissions and uptime requirements.

Hallucination and trust

Users stop relying on assistants that confidently return wrong answers. Grounding, citations and evaluation are engineering problems.

Disconnected from systems of record

An assistant that cannot read your CRM, ERP, ticketing or core systems — or act in them — adds a tab, not value.

Unpredictable cost and latency

Token spend and response times grow non-linearly without caching, routing, model selection and prompt discipline.

Security, privacy and governance

Sensitive data, access control, audit trails and model risk need design from day one, not a review at the end.

No way to measure improvement

Without offline evaluation sets and production telemetry, every change to a prompt or model is a guess.

Services

Shivacha AI services

Generative AI, AI agents, machine learning and automation engineered into the systems your business already runs on.

Generative AI & LLM applications

Assistants, copilots, chat and voice interfaces grounded in your knowledge through retrieval-augmented generation, with citations, guardrails and evaluation built in.

Generative AI & LLM Applications

6
+ 1 more

AI agents & automation

Agents that plan and execute multi-step work across your tools — triage, research, reconciliation, onboarding, document processing — with human approval where it matters.

AI Agents & Intelligent Automation

5

Machine learning & perception

Predictive models, NLP, computer vision and the data pipelines behind them, deployed with MLOps so models are monitored, retrained and versioned.

Machine Learning, NLP & Computer Vision

4

Enterprise AI & product

AI strategy, integration and full AI product development — from use-case selection and architecture to production operation.

Enterprise AI, Strategy & AI Products

5

Architecture

A reference architecture, adapted to you.

How we typically structure AI systems, adapted to your landscape and partners.

  • Assistants people actually useGrounded answers with sources, in the tools employees and customers already work in.
  • Automated multi-step workAgents that complete defined tasks and escalate exceptions, instead of just suggesting text.
  • Controlled costModel routing, caching and prompt design that keep unit economics predictable.
  • Governed AIClear data boundaries, audit logs and review workflows that satisfy security and risk teams.
Shivacha AI · reference stack
6Experience
Web & mobile appsCopilots in existing toolsChat & voiceAPIs
5Orchestration
Agent runtimeTool callingWorkflow engineHuman-in-the-loop
4Intelligence
LLMs (hosted & open-weight)Fine-tuned modelsClassical MLVision & speech
3Knowledge
RAG pipelinesVector searchEmbeddingsKnowledge graphs
2Data
Ingestion & ETLFeature storesData warehouseDocument stores
1Operations
Evaluation suitesObservabilityCost controlsAccess & audit

Approach

How we deliver.

  1. 1

    Use-case qualification

    We score candidate use cases on value, feasibility, data readiness and risk, and pick the ones that can reach production.

  2. 2

    Evaluation first

    Before building, we define what 'good' means with a test set and metrics, so every iteration is measured rather than judged by feel.

  3. 3

    Thin vertical slice

    We ship one narrow workflow end-to-end — data, retrieval, model, UI, logging — then widen it.

  4. 4

    Production hardening

    Guardrails, fallbacks, rate limits, caching, access control and monitoring are added before broad rollout.

  5. 5

    Operate and improve

    Telemetry, feedback capture and scheduled evaluation runs keep quality visible as data, users and models change.

Technology

Our stack for this practice

Technology directory
Large Language ModelsOpenAI ModelsRetrieval-Augmented GenerationAI AgentsVector DatabasesEmbeddingsFine-TuningMLOpsPythonFastAPI
Industries:
FinTechBankingInsuranceHealthcareE-commerceSaaSLogisticsEnterprise

Work & insights

Go deeper

All work
Reference architecture

Permission-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 more
Reference architecture

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 more
Insight · 9 min

Why most AI pilots never reach production — and the engineering that fixes it

The model is rarely the problem. Retrieval quality, evaluation, integration and governance decide whether an AI pilot becomes a production system.

Learn more

FAQ

Frequently asked questions

Which models do you work with?

We are model-agnostic. We build with hosted frontier models through their APIs and with open-weight models deployed in your cloud, and we often route between several models by task, cost and latency. The architecture is designed so that models can be swapped as the market moves.

Can our data stay inside our environment?

Yes. Retrieval indexes, vector databases, logs and — where required — open-weight models can run inside your own cloud account or VPC. We design data flows so that only the minimum context needed is ever sent to an external model provider, and only where your policies allow it.

How do you deal with hallucinations?

Through retrieval grounding, citations, constrained output formats, verification steps, refusal behaviour for out-of-scope questions, and — most importantly — an evaluation set that measures factual accuracy on your own questions before and after every change.

What does a first engagement look like?

Typically a two-to-four week discovery and prototype on one high-value workflow, with an evaluation set, followed by a production build. For clear requirements we can start directly with a production build or a dedicated AI team.

Do you build AI agents that take actions?

Yes. We build agents with explicit tool permissions, step limits, approval gates and full action logs. High-impact actions are routed to a human for approval until the agent's reliability on that task has been demonstrated.

Can you add AI to an existing product?

Most of our AI work is exactly that: adding copilots, search, summarisation, extraction or automation to platforms that already exist, through APIs and embedded UI components.

Request Technical Proposal.

Intelligent systems built for complex real-world workflows.

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