AI Integration Services
Integrate AI capabilities into existing products, ERP, CRM and internal systems through APIs, events and embedded UI.
- 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
Enterprise AI, Strategy & AI Products
Engagement
Project · Team · Managed
Overview
Most organisations do not need a new AI platform; they need AI inside the systems they already run. We integrate language models, ML predictions and AI automation into existing applications, CRMs, ERPs, helpdesks and data platforms — through APIs, event streams and embedded components — with the authentication, logging and cost controls those environments require.
Common use cases
- CRM enrichmentAI summaries, next-best-actions and drafting inside CRM records.
- ERP automationIntelligent document capture and anomaly detection feeding ERP workflows.
- Product feature integrationAI features added to an existing SaaS product.
- Data platform integrationAI enrichment jobs running on warehouse data.
Quick answers
AI Integration Services at a glance
The essentials in brief. Every project is scoped individually — ask us for specifics.
- What is AI integration services?
- Integrate AI capabilities into existing products, ERP, CRM and internal systems through APIs, events and embedded UI.
- 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?
- Integration architecture
- API & event integration
- Embedded UI components
- Identity propagation
- Central model gateway
- Fallback behaviour
- Which technologies are used?
- Artificial Intelligence, Large Language Models, Retrieval-Augmented Generation, AI Agents, MLOps, Microsoft Azure — chosen to fit your stack and constraints.
- How does the process work?
- Assess readiness → Prioritise use cases → Build the foundation → Deliver the first wave → Scale the operating model.
- 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
Integration architecture
Where AI runs, how it is called and how results flow back.
API & event integration
Synchronous APIs and asynchronous event-driven processing.
Embedded UI components
Assistants and suggestions inside existing interfaces.
Identity propagation
User permissions respected in AI calls.
Central model gateway
Unified access, logging and cost control.
Fallback behaviour
Graceful degradation if AI services are unavailable.
Architecture
Engineered right from day one
The layers we typically design for enterprise AI, strategy & AI products, adapted to your stack and partners.
- Avoid the pilot trapEvery initiative has a named owner, a production path and success metrics before it starts.
- Shared infrastructureA central model gateway prevents every team from reinventing access, logging and cost control.
- Policy alignmentAcceptable-use, data-handling and review policies are designed with security and legal teams.
- Change managementAdoption plans and training are part of delivery, not an afterthought.
Delivery
How an engagement runs
- 1
Assess readiness
Data, systems, skills, policies and appetite for change.
- 2
Prioritise use cases
Score by value, feasibility and risk; pick a balanced first wave.
- 3
Build the foundation
A shared model gateway, retrieval and evaluation stack reused across use cases.
- 4
Deliver the first wave
Production launches with measured outcomes, not slideware.
- 5
Scale the operating model
Governance, reusable components and team enablement for the next waves.
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.
Products
Start from a platform
Shivacha AI Sales Agent
An AI sales assistant that qualifies, researches and books — inside your CRM.
Learn moreShivacha AI Analytics
Ask questions of your data in plain language — with governed, verifiable answers.
Learn moreShivacha Copilot Kit
Embed a context-aware AI copilot into your product or internal tools.
Learn moreRelated services
Often combined with
AI Development
End-to-end AI development: from use-case selection and data preparation to models, applications and production operations.
Learn moreEnterprise AI Solutions
Enterprise AI programmes: shared AI platforms, governance, security and a portfolio of production use cases across the organisation.
Learn moreAI Consulting
Practical AI consulting from engineers: use-case prioritisation, architecture, build-vs-buy, vendor selection and roadmaps.
Learn moreDedicated team
AI Engineering Team
AI engineers who build production LLM applications, RAG systems and AI features.
Work & insights
Related thinking
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 moreMulti-tenant SaaS foundation built for enterprise readiness
The foundations we put in place when building a SaaS product that will need to sell to enterprises.
Learn moreWhy 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 moreFAQ
Frequently asked questions
Can AI be added to legacy systems?
Yes, typically through APIs, database integration or event capture, with AI services running alongside rather than inside the legacy code.
What if the AI provider has an outage?
We design fallbacks — alternative models, cached responses or graceful degradation — so core workflows keep functioning.
Where should an enterprise start with AI?
Usually with two or three use cases that have clear owners, accessible data and measurable value — often internal knowledge assistance, document-heavy processes or customer support — built on shared foundations that later use cases can reuse.
Do you provide AI governance frameworks?
We help design practical AI governance — use-case risk tiering, review workflows, logging and monitoring — aligned with your existing risk and compliance processes. We do not provide legal advice on AI regulation.
Next step
Build Your AI Product.
Tell us about your AI integration services 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.