AI Product Development
Build AI-native products and SaaS: product strategy, UX for AI, model architecture, evaluation, metering and scale.
- 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
AI-native products have distinct design challenges: communicating uncertainty, designing for review and correction, managing variable inference cost within a pricing model and improving quality from usage data. We build AI products from concept to scale — product strategy, UX patterns for AI, model and retrieval architecture, evaluation, usage metering and the SaaS foundations around them.
Common use cases
- Vertical AI SaaSAI products for a specific industry workflow.
- AI features in existing productsNew AI-powered modules or tiers.
- API-first AI productsAI capabilities exposed as developer APIs.
- Internal AI productsTools built for employees with product-grade quality.
Quick answers
AI Product Development at a glance
The essentials in brief. Every project is scoped individually — ask us for specifics.
- What is AI product development?
- Build AI-native products and SaaS: product strategy, UX for AI, model architecture, evaluation, metering and scale.
- 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?
- Product discovery for AI
- AI UX design
- Unit economics
- Metering & billing
- Quality flywheel
- SaaS foundations
- 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
Product discovery for AI
Validating that AI solves the problem better than alternatives.
AI UX design
Patterns for suggestions, confidence, review and correction.
Unit economics
Inference cost modelling against pricing tiers.
Metering & billing
Usage tracking, quotas and entitlements.
Quality flywheel
Feedback capture that improves prompts and models.
SaaS foundations
Multi-tenancy, auth, billing and admin.
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 SaaS Starter
The foundations of an AI SaaS product — ready to build your differentiation on.
Learn moreShivacha Copilot Kit
Embed a context-aware AI copilot into your product or internal tools.
Learn moreShivacha Agent Platform
Build, govern and operate AI agents that do real work across your systems.
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 moreAI Integration Services
Integrate AI capabilities into existing products, ERP, CRM and internal systems through APIs, events and embedded UI.
Learn moreEnterprise AI Solutions
Enterprise AI programmes: shared AI platforms, governance, security and a portfolio of production use cases across the organisation.
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
How do we price an AI product with variable costs?
Through usage-based components, tiered quotas, model routing to control cost, and caching. We model margins at different usage levels before launch.
How do AI products improve over time?
By capturing user feedback and corrections, adding them to evaluation sets and using them to refine prompts, retrieval and models.
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 product development 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.