Kirikaa Digital engineers custom AI systems around your data, your workflows and your systems — from AI agents and RAG to voice, document AI, automation, integrations and private deployment.
A general model is a starting point, not a solution. Custom AI is built so the intelligence fits the problem — your terminology, your rules, your systems.
Tap each gap a generic model can't handle on its own — and see what custom AI adds to close it.
Select the gaps that match your problem.
Each layer closes a class of gaps.
Custom AI isn't a bigger model — it's the right model wrapped in the knowledge, tools, workflow and controls your business actually needs. The more gaps you select, the clearer the case for a custom build.
Six layers, combined to fit the problem. Most production AI systems use several — not all.
The intelligence layer — chosen per use case: commercial LLM, open-source, BharatLM, or a small domain model.
Your documents, policies and data, structured and indexed so the AI answers from what you own.
APIs and functions the AI can call — to look things up, calculate, and take actions in your systems.
Orchestration that plans steps, uses tools, observes results, and works through a task end to end.
The product surface — web, mobile, chat, voice or embedded — where users meet the AI.
Access control, permissions, policy checks, monitoring and human approval — the controls that keep it safe.
The right AI solution combines the right layers — not all of them.
From raw data to monitored production — click any stage to see what we do there.
We assess, clean and structure your data — the foundation every reliable AI system is built on. Without a data strategy, the rest of the stack is built on sand.
Choose the capability you need — or let Kirikaa design the complete AI architecture.
Everyday help grounded in your business
An assistant that answers questions, drafts work and helps employees get things done, grounded in your knowledge and systems.
Support, sales, operations and internal teams who need fast, consistent answers.
Knowledge bases, CRM, helpdesk, internal docs and APIs.
Employee asks a question → AI retrieves the relevant policy → answers with a citation → logs the interaction.
Every custom AI system is a set of layers working together. Click any node to see its role.
Plans steps, routes to models and tools
The control layer that decides what to do next — routing to models, tools, and knowledge.
An agent framework or a custom orchestration layer, depending on complexity.
Over-engineering a simple task, or under-engineering a complex one.
We don't start with a model. We start with the business problem — then choose the foundation that fits.
Commercial LLMs for broad reasoning, drafting and analysis.
Models specialized for a field — healthcare, finance, legal and more.
Compact models for high volume, low latency, low cost.
Models that work across text, image, audio and video.
Self-hosted open models for control and customization.
Kirikaa's foundation for Indian business contexts and languages.
A model adapted to your data, style and behavior.
We don't start with a model. We start with the business problem — then choose the foundation that fits.
Retrieval-augmented generation grounds the model in your documents — so it answers from what you know, and can show its work.
An agent doesn't just answer — it plans, uses tools, observes, and acts. Here's the loop it runs.
The loop repeats until the task is complete — with human approval where the action is sensitive.
When a task is complex, an orchestrator coordinates specialized agents — each with its own tools and scope.
Real business information comes in many forms. Multimodal AI takes them all in and produces what your workflow needs.
For phone, mic and in-app channels — the AI listens, understands, uses tools, and responds by voice.
From a scanned invoice to a structured record in your system — extraction, understanding, and routing.
Accuracy depends on document variety and quality — we validate against your real samples, not a promise of perfect OCR.
The best AI appears where the work happens. We embed intelligence into the applications your teams already rely on.
CRM, ERP, e-commerce, mobile, web and portals — each becomes smarter when AI is woven in. The capabilities we add:
Intelligence that can only advise is limited. AI that can act — within your controls — changes the outcome.
BharatLM is Kirikaa's proprietary AI model foundation, built to understand Indian business contexts and languages. It can be the right base for domain-specific, Indian-language, or privately-controlled AI — not automatically the best choice for every use case.
Proprietary foundation for domain-specific and controlled AI applications.
V5: 6B total params, 1.6B active, 1M-token context, adaptive denoising.
1,000+ tokens/sec on an A100 — a target, not a guaranteed benchmark.
22+ Indian languages and scripts; OpenAI-compatible API, function calling.
The same AI system can run in several places. Choose the model that fits your data, security and control needs.
Fastest to start. The AI runs on a managed cloud service; your application calls it over the network.
Your own isolated cloud environment. Data stays within your controlled boundary.
Everything runs inside your own data center. Maximum control over data and inference.
Inference runs close to where the data is generated — on a device or local node. Low latency, high privacy.
AI is built directly into your product — a feature, not a separate service.
When data sensitivity, residency or control matter, the AI runs where you decide — your data, your infrastructure, your AI.
Deployment control is an engineering and architecture decision. We design it around your requirements — we do not provide legal or compliance guarantees.
Control isn't an afterthought. Access, data, tools, operations and human oversight are designed in from the start.
Every action the AI can take is scoped by permission. Sensitive or high-stakes actions pass through a policy check and, where you require it, a human approval gate before they happen.
We measure what matters for your use case — then improve in a loop, not a one-off.
A domain-specific benchmark — measured on your real data, not a generic leaderboard.
The same engineering, shaped to your industry's workflows. These are potential use cases — not claims of existing deployments.
Document processing, clinical summarization and administrative automation — with human review on clinical decisions.
Fraud signals, document processing, customer support and compliance workflows.
Contract review, document summarization and legal research assistance.
Quality inspection, predictive maintenance and operational intelligence.
Personalized learning, content generation and administrative automation.
Citizen services, document processing and public-data intelligence — with sovereignty and control.
Claims processing, underwriting assistance and customer support.
Recommendations, customer support, content and demand intelligence.
A reference architecture showing how the pieces come together — from a document to a governed, structured business action. Click any step to see what happens.
A business document — invoice, contract, claim, form — enters the system. It is the raw input the whole pipeline works from. IN: PDF / image / file · OUT: normalized document.
This is a reference architecture — a pattern we adapt to your real systems, not a fixed product.
A disciplined path — no black boxes, no skipped steps.
Conceptual reference architectures we design and adapt — organized by the kind of AI system they represent.
Reference architectures shown for illustration. See our portfolio for the work we have shipped.
Four ways to work with us — from a focused discovery to a full enterprise AI platform.
A focused engagement to map your workflows, data and the highest-value AI use cases.
A working prototype that proves the concept on your real data, fast.
A full, production-grade AI system — built, evaluated, deployed and integrated.
A long-term partnership to build and run an enterprise-wide AI platform.
Eight reasons we're the partner for serious, production AI.
Deep, hands-on AI engineering — not just prompt wrappers.
Every system starts from a real business problem, not a model.
We pick the right model for the job — cloud, open or BharatLM.
AI shaped to your industry's data, language and workflows.
Production-grade software, not just a model in a notebook.
Tool-enabled, multi-step agents that actually do work.
Cloud, private, on-prem or edge — your choice, your control.
Evaluation and iteration built in, not bolted on.
A custom AI solution is an AI system built specifically around your business — your data, workflows and systems — rather than a generic off-the-shelf tool. It is architected, built and integrated for your exact use case.
ChatGPT is a general-purpose model you talk to. A custom AI solution is a system built around your business: it connects to your data and systems, follows your rules, uses your tools, and is evaluated on your real tasks. The model is one component; the architecture and integration are what make it custom.
It depends on scope, data, integration and deployment. We provide a custom quote after a discovery conversation — there is no fixed price for a system built around your business.
A proof of concept can be built in weeks; a full production system takes longer depending on integration and evaluation depth. We scope a realistic timeline during discovery.
Yes — that's the core of what we do. We map your process, identify where AI adds value, and build the system to fit how you actually work.
Yes. Depending on the use case we may fine-tune a model, specialize it with your domain data, or ground it with RAG. We choose the approach that fits your data and goals.
Often both. RAG grounds the model in your current knowledge; fine-tuning shapes its behavior and style. We recommend the right mix based on your data, latency and accuracy needs.
Yes. We build a knowledge layer over your documents so the AI can retrieve and cite from them — keeping answers grounded in your actual content.
Yes. We integrate the AI with your CRM, ERP and other systems so it can read from and act on them through controlled, permissioned tool calls.
Yes. The AI can call your internal and external APIs as tools, with scoped permissions and policy checks on what it's allowed to do.
Yes. We build tool-enabled agents that can plan, call tools and complete multi-step tasks — with governance and human oversight where you need it.
Yes. For complex workflows we design multi-agent systems where specialized agents coordinate to complete larger tasks.
Yes. We build voice AI — speech-in, speech-out — with grounding, tool use and natural conversation, for support, intake and more.
Yes. We build document AI that extracts, classifies and structures data from invoices, contracts, claims and other business documents.
Yes. We build systems that work across text, images, audio and video — for use cases like visual inspection, media understanding and more.
Yes. We integrate AI directly into your existing applications as a feature — not a separate tool your team has to switch to.
Yes. BharatLM is our proprietary foundation, well suited to Indian-language and domain-specific, controlled AI. We use it when it's the right fit — not automatically for every use case.
Yes. We deploy in private cloud, on-premise or edge, so the AI runs within your controlled boundary when data sensitivity or residency matters.
Yes. On-premise deployment runs everything inside your own data center, giving you maximum control over data and inference.
Yes. BharatLM targets 22+ Indian languages and scripts, and we build systems for Indian-language contexts where it's the right fit.
With a domain-specific benchmark measured on your real data — accuracy, relevance, hallucination, tool selection, structured output, latency, cost, safety and task completion — then improved in a loop.
Through grounding (RAG), structured output, evaluation and guardrails. We measure hallucination on your data and reduce it with the right architecture — we don't claim zero hallucinations.
Yes. Access control, tool permissions and policy checks scope exactly what the AI is allowed to do — designed in from the start.
Yes. For high-stakes actions we build human approval gates so a person reviews and approves before the action is finalized.
Yes. We wire AI decisions into your existing automation and business processes so it works with how you already operate.
Yes. A PoC is a great way to start — a working prototype on your real data, with a measured go / no-go recommendation.
Yes. Production AI needs monitoring, evaluation and improvement. We offer ongoing support and continuous improvement as part of an enterprise engagement.
Yes. We can take an existing AI system and modernize it — better architecture, evaluation, governance and deployment — rather than starting from scratch.
Tell us about your business, your data and the outcome you want. We'll map the AI architecture that fits — and how to get there.
We map your problem, data and systems.
A reference architecture and roadmap.
Engineered, governed, in your stack.