Kirikaa Digital is your AI strategy partner. We assess your business, identify high-impact AI opportunities, and guide implementation end to end — from the first decision to production.
Unlike strategy-only consultancies, Kirikaa can carry recommendations into actual software and AI implementation.
Most leaders are not blocked by a lack of AI ideas — they are blocked by too many of them, and no clear way to tell which ones are worth the investment. These are the patterns we see again and again.
Every team has a list of AI ideas, but no way to tell which ones actually move the business.
LLMs, RAG, agents, fine-tuning, open vs closed models — the jargon makes it hard to decide.
AI plans that assume clean, connected data that does not yet exist, so projects stall early.
Investment is hard to justify when nobody can say what the AI is expected to return.
Endless pilots that never reach production because there was no path from experiment to value.
Valid worries about data, privacy, and control that slow decisions down or block them entirely.
Choices driven by a single vendor's roadmap rather than your business requirements.
Even a good plan needs people who can run, extend, and own the system over time.
AI strategy turns uncertainty into a prioritized action plan.
A strategy engagement is a lifecycle, not a report. Each stage produces something you can act on, and the whole thing ends with a plan your team can execute.
Learn your business, goals, constraints, and how work actually flows today.
Evaluate data, systems, processes, and readiness across every dimension.
Rank opportunities by value and complexity to decide what to do first.
Choose the model, data, and architecture that fit each use case.
Prove the approach on a small, real slice before committing at scale.
Turn the validated plan into a phased, resourced, time-bound roadmap.
Carry the plan into build and deployment, from pilot to production.
We map candidate use cases on a value-versus-complexity matrix. Select any use case to see the problem it solves, the approach, what it needs, and the recommended next step.
High value · Low complexity · Do first
Inbound leads wait for manual review, so hot prospects cool down and sales capacity is spent on low-intent enquiries.
An AI assistant reads each enquiry, scores intent against your criteria, enriches the record, and routes qualified leads to the right team.
Lead records, enquiry text, product and pricing information, and your qualification criteria.
CRM, website forms, email, and chat channels.
Low to medium — a focused RAG or classification setup with clear handoff to sales.
Faster response to high-intent leads and less time spent on low-intent ones. We validate the actual impact during the pilot rather than quoting a number.
Start with a short discovery session to map your current lead flow and qualification rules.
Illustrative use cases for demonstration. Your assessment is built from your own business, and we do not quote invented ROI figures.
Readiness is not a single score. We review six dimensions, each against specific criteria, and mark it ready, partial, or needing work. Select a dimension to see what we check.
We report readiness qualitatively — ready, partial, or needs work — rather than a fake numerical score.
Every candidate use case lands in one of four quadrants, and each quadrant has a clear recommended action.
Start here. These use cases deliver value fast and build organizational confidence in AI.
Worth pursuing, but sequence them carefully with proper architecture, data work, and governance.
Low risk and low effort, but limited payoff. Pick them up only if capacity allows.
These consume budget and attention without meaningful business impact. Deprioritize them.
There is no default answer. The right choice depends on seven factors — select one to see how it shifts the decision.
Off-the-shelf AI products and platforms.
Custom AI systems built for your business.
Buy the commodity, build the differentiator.
We recommend technology based on the business requirement — not vendor preference.
Model choice follows a decision path, not a brand preference. Select a category to see its strengths and trade-offs.
Frontier models from global providers.
Broad capability, fast access, strong general performance.
Per-use cost, data leaves your control, vendor terms apply.
Models you can host and adapt yourself.
Self-hosting, customization, no per-token vendor fees.
You own infrastructure, tuning, and maintenance.
Kirikaa's domain-specific foundation.
Indian business context and languages, controlled deployment, OpenAI-compatible API.
Best fit for domain-specific and language-focused workloads, not every use case.
Lightweight models tuned to one task.
Fast, cheap, predictable for narrow, high-volume tasks.
Narrow scope; needs retraining as the task changes.
Models that read text, images, and more.
Handles documents, photos, and mixed inputs in one system.
Higher cost and complexity than text-only models.
Speech recognition and generation.
Phone and voice-first workflows, natural conversations.
Latency and accent handling need careful design.
Image and video understanding.
Inspection, document reading, and visual workflows.
Accuracy depends heavily on image quality and variety.
Grounding any model in your knowledge.
Keeps answers grounded in your data; updates without retraining.
Requires clean, well-indexed knowledge sources.
A model is only as good as the information it can reach. We map your sources, prepare the data, and choose the right way to feed it to the AI.
Retrieve relevant knowledge before each answer. The default for most business AI.
Adapt a model to a narrow task or style. Used when prompting is not enough.
Shape behavior with instructions and examples. Fastest to start.
Classic analytics and rules where tables and logic are the right tool.
Combine keyword and semantic search for the most reliable results.
Not every AI problem requires fine-tuning. We choose the simplest approach that meets the requirement.
Every layer of an AI system is a decision. Select a layer to see its purpose, the technology options, the risks, and the criteria we use to choose.
Automation has a progression. We start at the simplest level that works and only add intelligence where it earns its place.
Fixed triggers, rules, and actions. Best for predictable, structured steps.
Understands natural language and answers or drafts, but does not act on its own.
Plans multi-step work, uses tools and your systems, and completes real tasks.
Specialized agents coordinated by an orchestrator for complex, cross-domain workflows.
More intelligence is not always better. The right architecture is the simplest one that solves the problem.
Governance is not a document on a shelf. It is a set of controls across seven areas, run as a continuous loop.
Access, quality, retention, and privacy of the data the AI uses.
Which models are approved, where they run, and how they are updated.
Which systems the AI may touch, and what it is allowed to do in them.
Who owns the AI, who can change it, and who is accountable.
Logging, audit trails, and performance and drift tracking over time.
Known failure modes, edge cases, and what happens when the AI is unsure.
Written rules for acceptable use, approvals, and escalation.
Human gates placed where decisions are sensitive or high-stakes.
Leadership does not invest in technology — they invest in outcomes. We frame the value, the cost, the risk, and the time to value so the decision is grounded in your numbers, not ours.
We do not fabricate ROI or savings figures. The business case is built from your data and validated during the pilot.
A roadmap is a sequence of phases, each with a clear goal and deliverable. Hover a phase to see what it produces.
Understand the business, data, and candidate opportunities.
AI opportunity map and readiness assessment.
Rank use cases and design the first pilot.
Prioritized list, pilot plan, and business case.
Build and validate the priority use case with real users.
Working pilot with measured results.
Move the validated pilot into dependable production.
Production system with monitoring and governance.
Extend to more use cases, channels, and teams.
Scale plan and next-wave roadmap.
Illustrative phases, not guaranteed timelines. Your roadmap is scoped to your business.
Most AI projects stall between experiment and production. We prevent that with promotion gates — each stage must pass a clear question before the next begins.
Prove the core capability on a small, real slice of the problem.
Run with real users and measure whether the benefit is real and repeatable.
Hardening, monitoring, governance, and support before it carries real load.
Confirm unit economics hold as volume grows, then expand.
BharatLM is Kirikaa's proprietary AI model foundation, built to understand Indian business contexts and languages. It is one option in the model strategy — the right one for specific workloads, not automatically the best for every use case.
Models tuned to your industry's language, workflows, and context.
Strong performance across Indian languages and business contexts.
Run the model where your data and compliance requirements demand.
Customization for tasks where general-purpose models fall short.
BharatLM is one option in a model-neutral strategy. We recommend it when the workload fits — and point you elsewhere when it does not.
AI strategy is not one-size-fits-all. Select an industry to see the strategy areas we typically map for it.
AI strategy for healthcare focuses on reducing administrative load and supporting clinical knowledge — always with human oversight for patient-facing decisions.
Patient-facing and clinical decisions require human oversight and regulatory compliance.
Finance AI strategy centers on document-heavy workflows, compliance, and customer service at scale.
Financial decisions and advice require human oversight and regulatory compliance.
Legal AI strategy is built around document intelligence and fast, accurate knowledge retrieval across large bodies of work.
Legal advice and filings remain under professional human review.
Manufacturing AI strategy targets operational knowledge, maintenance, and quality — where institutional expertise lives in people's heads.
Safety-critical decisions remain with trained operators and engineers.
Education AI strategy supports students and staff with knowledge assistants and learning systems that scale beyond classroom hours.
Assessment and certification decisions remain with educators.
Government AI strategy focuses on citizen services, document processing, and knowledge systems that must be transparent and accountable.
Public decisions require human oversight, transparency, and compliance.
Retail AI strategy improves the customer journey — from support and product intelligence to personalization at scale.
Personalization must respect privacy and consent requirements.
Insurance AI strategy accelerates claims and document workflows while keeping decisions auditable and fair.
Claims decisions require human oversight and regulatory compliance.
These are potential strategy areas, not claims of existing deployments.
A reference engagement: a 50-person company receiving thousands of customer enquiries across email, phone, and web.
Enquiries arrive by email, phone, and web forms. Records live in a CRM, answers in PDFs, and qualification is done manually.
Customer support, lead qualification, and internal knowledge search emerged as the highest-impact areas.
1. Customer support assistant · 2. Lead qualification · 3. Internal knowledge assistant.
RAG over company knowledge, an AI model for answers, CRM integration, and a human handoff for complex cases.
A website AI assistant answering from company knowledge, with human escalation.
Website, WhatsApp, and CRM-connected support with monitoring and governance.
Voice support, deeper automation, and domain specialization as the foundation matures.
Reference consulting engagement, shown to illustrate the shape of the work. Not a claim of a specific client or deployment.
Eight stages, each producing something concrete. The flow is simple: questions become insights, insights become decisions, decisions become a roadmap, and the roadmap gets executed.
Stakeholder conversations to understand goals, constraints, and how work flows today.
Review of data, systems, processes, and existing AI usage.
Identification of candidate use cases across the business.
Ranking by value and complexity into a do-first, plan, optional, avoid map.
Model, data, and system design for the priority use cases.
Value, cost, risk, and time-to-value framed for leadership.
A phased, resourced plan from pilot to production to scale.
Carrying the plan into build and deployment, from pilot to production.
Every engagement produces a set of working artifacts — not a single report that sits in a folder.
Every candidate use case, ranked by value and complexity.
Dimension-by-dimension view of what is ready and what needs work.
Layer-by-layer design of the AI system, with options and risks.
Which model family fits each use case, and why.
Sources, preparation, and retrieval approach for each system.
Value, cost, risk, and time-to-value for leadership sign-off.
Phased plan from discovery to scale, with deliverables per phase.
Scope, success criteria, and promotion gates for the first pilot.
Controls, approvals, and monitoring for safe, accountable AI.
These are reference architectures we design and build — conceptual patterns that show the depth of the work, not claims of specific client deployments.
RAG over company knowledge with human handoff — a reference architecture for support AI.
Answers from internal documents, SOPs, and policies — a reference architecture for knowledge AI.
Extract, structure, and route documents with AI — a reference architecture for document AI.
Score and route inbound leads with AI — a reference architecture for sales AI.
Domain knowledge in Indian languages on a controlled foundation — a reference architecture for BharatLM.
An agent that plans and executes multi-step work across systems — a reference architecture for agent AI.
Reference architectures shown for illustration. See our portfolio for the work we have shipped.
From a single discovery conversation to a full strategy-and-build partnership, there is a level that fits where you are today.
A focused conversation to map your AI questions and immediate opportunities.
A structured review of your business to find and rank AI opportunities.
The full strategy: architecture, model and data strategy, business case, and roadmap.
Strategy plus the build — we carry the plan from pilot to production with you.
Strategy is only as good as the ability to execute it. Here is what makes Kirikaa different from a strategy-only consultancy.
Our strategy is written by people who build and ship software and AI systems, not just advise on them.
Every recommendation starts from your business goals and constraints, not from a technology we want to sell.
We recommend the model that fits the job — commercial, open-source, or BharatLM — without vendor bias.
Access to Kirikaa's proprietary model foundation for domain-specific and Indian-language workloads.
From data and architecture to models, agents, and deployment — the whole stack under one roof.
We don't just tell you what to build. We can build it with you, from pilot to production.
We define the metrics up front and validate against your real data — no fabricated ROI.
AI adoption is a journey, not a project. We stay with you as the system grows.
The questions we hear most often about AI strategy, readiness, and implementation.
AI consulting is the strategic layer of AI adoption. We assess your business, data, processes and goals to identify where AI can create real value, then help you prioritize, design, and plan the implementation — so decisions are grounded in your business rather than technology hype.
AI development builds a specific system. AI consulting decides what to build, why, and in what order. It covers opportunity assessment, readiness, model and architecture strategy, business case, and roadmap. We can also carry the recommendations into actual build and deployment.
It is a structured review of your business to find use cases where AI can move the needle, then rank them by value and complexity. Each candidate is evaluated for the problem it solves, the data and systems it needs, its complexity, and its expected value — without invented ROI figures.
It evaluates whether your organization is ready to adopt AI across six dimensions: data, process, technology, people, governance, and business. Each dimension is reviewed against specific criteria and marked as ready, partial, or needing work — so you know what to fix before investing.
We place each use case on a value-versus-complexity map. High value and low complexity ideas are done first; high value and high complexity ideas are planned carefully; low value ideas are deprioritized. Factors include business value, data availability, risk, time to value, and integration effort.
It depends on cost, time, control, differentiation, data, security, and scalability. Some needs are best met by off-the-shelf tools, some by custom builds, and many by a hybrid. We recommend technology based on the business requirement, not vendor preference.
We work through a decision path: use case, language, context, latency, cost, privacy, and deployment. Depending on the answer, the right foundation may be a commercial LLM, an open-source model, a domain-specific foundation like BharatLM, a small domain model, or a multimodal, voice, or vision model.
It is the plan for getting the right information to the model: identifying data sources, assessing and cleaning them, structuring and indexing them, and retrieving the most relevant pieces at decision time. It also decides between RAG, fine-tuning, prompting, structured data, or hybrid retrieval.
It is designing the AI system before you build it — the user layer, application, orchestrator, model, knowledge and tools, data and APIs, business logic, actions, and human approval, plus security, monitoring, and governance. Each layer is chosen with its purpose, options, and risks in mind.
Not always. We progress from rule-based automation, to an AI assistant, to an AI agent, to a multi-agent system, and stop at the simplest level that reliably solves the problem. More intelligence is not always better.
Through governance across data, model, tools, people, monitoring, risk, and policy, run as a continuous loop of design, control, monitor, review, and improve. Human approval gates are placed where decisions are sensitive or high-stakes.
We frame the value (cost reduction, productivity, revenue opportunity, experience, risk reduction, speed), the cost (model, infrastructure, integration, development, maintenance), the risk, and the time to value, so leadership can make an informed investment decision. We do not fabricate ROI numbers.
A practical, phased plan: discover, prioritize, pilot, move to production, then scale. An illustrative shape is a discovery phase, a priority pilot, a production phase, and a scale phase. These are illustrative phases, not guaranteed timelines.
Through promotion gates. A proof of concept must show it works, a pilot must show users benefit, production must show the business can depend on it, and scale must show the economics are sustainable. Each gate is a go/no-go decision.
BharatLM is Kirikaa's proprietary AI model foundation for domain-specific and controlled AI applications, built to understand Indian business contexts and languages. It can be the right foundation for domain-specific AI, Indian language applications, private or controlled deployment, and specialized workloads. It is not automatically the best choice for every use case.
The V5 architecture is documented as 6B parameters with 1.6B active, a 1M token context, and a target of 1,000+ tokens per second on an A100 (a target, not a guaranteed benchmark). The v3 production blueprint offers an OpenAI-compatible API, function calling, JSON-schema output, and India-based processing.
Yes. We map strategy to your industry's workflows — for example healthcare, finance, legal, manufacturing, education, government, retail, and insurance. These are potential strategy areas, not claims of existing deployments, and regulated industries include human-oversight considerations.
A typical engagement moves from current state, through discovery, opportunity mapping, prioritization, architecture, and a reference pilot, to production and scale. We illustrate this with a reference consulting engagement for a company receiving thousands of customer enquiries.
Eight stages: discovery, current-state assessment, AI opportunity mapping, prioritization, architecture, business case, roadmap, and execution support. The flow is questions, insights, decisions, roadmap, execution.
An AI opportunity map, readiness assessment, architecture blueprint, model strategy, data strategy, business case, AI roadmap, pilot plan, and governance framework — a set of artifacts you can actually execute, not just a document.
Four levels: an AI discovery session, an AI opportunity assessment, an AI strategy and roadmap, and AI strategy plus execution. Pricing is a custom quote scoped to your needs.
Because our consulting is engineering-backed and business-first. We are model-neutral, have access to BharatLM, cover the full AI stack, are implementation-ready, focus on measurable outcomes, and act as a long-term partner. We don't just tell you what to build — we can build it with you.
A discovery session is a single focused conversation. A full strategy and roadmap typically spans several weeks depending on scope, data access, and stakeholder availability. Timelines are agreed up front and are illustrative, not guaranteed.
Pricing is a custom quote. It depends on the depth of assessment, the number of use cases and systems reviewed, and whether execution is included. We scope each engagement individually so you pay for what you actually need.
No. We do not fabricate ROI, cost savings, or productivity figures. We help you define the value you are aiming for and the metrics to measure it, then validate against your real data during the pilot.
Yes. That is a core part of the offer. We can carry the recommendations from strategy and architecture into actual software and AI implementation, from pilot to production, so the plan and the build stay aligned.
We handle your data according to your policies and applicable regulations, scope access to what is needed, and design governance, privacy, and security controls into the strategy from the start rather than adding them later.
That is exactly what the readiness assessment is for. If data, process, or governance needs work first, we tell you plainly and sequence the plan so you invest in the right order instead of forcing a pilot that cannot succeed.
Start with a conversation. Tell us where you are today and what you are trying to achieve, and we will show you where AI can create real value — and where it cannot.