Named, owned and observable.
AI opportunity and implementation partner
Know where AI will pay off.
Before you pay to build it.
We find the workflows quietly costing your business time and money, prove what is worth fixing, and build only when the case is clear.
01You own every recommendation.
02AI is never the default answer.
03One measurable workflow first.
04Founder-reviewed, engineer-tested.
Free mini assessment
Start with the work that feels stuck.
Choose one answer. We will show you the likely opportunity before asking how to contact you.
01 · The real problem
Most businesses do not have an AI problem. They have an expensive workflow problem.
What the team experiences
What Vikalp isolates
Time, cost, error, delay or missed revenue.
Fix, automate, use AI, or do not build.
02 · Selected work
We do not just advise. We build and operate.
Real products and working AI systems, built by members of the Vikalp founding team around specific operating problems.
Live product
AI-enabled women’s health community
AI inside the product. Agents behind the operation.
Cysters Club combines a member-facing AI companion with focused agents that help the team create, publish and grow. Each system owns a defined workflow instead of trying to automate the entire business.
Member AI companion
SHEA gives women with PCOS and PCOD a supportive, context-aware place to ask questions and understand their health journey.
Content operations agent
Turns structured briefs into on-brand content, prepares it for review, then automatically publishes approved posts inside the mobile app.
Lead generation agent
Supports the growth team by finding, organising and qualifying opportunities so people can focus on the conversations that matter.
Selected work by members of the Vikalp founding team. No performance figures are stated until a measured baseline is available.
Client case · Legal workflow
Working systemFrom a case file to a lawyer-reviewed working draft.
We built focused tools for a practising advocate who needed useful preparation without enterprise legal software. The AI organises the material and prepares the work. The advocate reviews, changes and owns every legal decision.
Property-document preparation
Upload the relevant property documents, describe the required task, and receive a structured working draft tailored to the matter for advocate review.
Cross-examination preparation agent
Organises matter facts, witness objectives, contradictions and evidence gaps into a reviewable question strategy. It supports preparation; it does not replace legal judgement.
Built for and used by C. S. Srinivas, an advocate in Bengaluru with 30+ years’ experience. This case documents a real, lawyer-supervised workflow, not autonomous legal advice. No client identities, matter details or unverified performance figures are disclosed.
03 · The approach
Four decisions. No forced journey.
Every stage creates something useful on its own. Continue only when the evidence supports the next investment.
Find the signal.
Qualify one painful, frequent and measurable workflow.
Prove the case.
Map the process, economics, options, dependencies and risk.
Ship one system.
Use clear scope, human controls and acceptance criteria.
Keep it valuable.
Monitor reliability, quality, cost and improvements.
04 · The paid audit
A decision document. Not a disguised sales deck.
The audit shows what is happening now, what the problem costs, which options make sense, and what should happen next. Take it to us, another builder, or your internal team.
- Workflow and bottleneck map01
- Value model with visible assumptions02
- AI versus automation decision03
- Risk and dependency register04
- Build-ready implementation roadmap05
AI opportunity score
High value · manageable first scope
- Automate enquiry qualification and routing
- Prepare follow-up drafts with human approval
Faster response and less repetitive follow-up. Exact savings require baseline validation.
Illustrative audit example · score shown only after evidence review
05 · Implementation
One workflow. Built from signal to verified outcome.
New request received. Source and intent captured.
00:00.206 · Managed AI operations
Launch is day one.
Models change. Inputs drift. Edge cases appear. We maintain what we build only when the system has recurring operational work.
Failures, quality, cost and latency
01Integrations, documentation and recovery
02Prompts, models, rules and workflows
03Business outcomes against the baseline
0407 · Who is accountable
A commercial lead and two engineers, close to every decision.
Founder
Research, buyer conversations, audits, scope and client outcomes.
Systems engineer
Architecture, integrations, infrastructure and production reliability.
AI quality engineer
Evaluation, retrieval, controls, test data and failure handling.
08 · The starting conditions
Good AI begins with a real operational ache.
✓ Strong fit
- The workflow happens often enough to measure
- Delay, rework or manual effort has a real cost
- A process owner can help us understand the work
- Your team is willing to start with one workflow
× Not yet
- You want a broad AI transformation with no priority
- The goal is replacement without process evidence
- No baseline or accountable owner is available
- You need guaranteed savings before discovery
Start with the smallest useful decision
Bring us the work your team is tired of.
Free mini assessment · Founder reviewed · Human response
09 · Contact
Talk to a human. Not a chatbot.
Tell us the workflow slowing your team down. We reply personally, usually within a working day.