Retrieval over your knowledge
Docs, help centre, PDFs, past tickets, product data — chunked, retrieved, and cited at answer time so replies stay grounded instead of merely sounding right.
What we build
The layers we assemble. Not a product you subscribe to.
ClickToAutomate ships fitted software into the CRM, helpdesk, WhatsApp, catalogue, and documents a company already pays for. Retrieval with citations, support with human hand-off, lead qualification into the CRM, and ops agents with guardrails — a live product on the desk, not a generic chatbot or a seat license.
Per engagement
Nothing here is a fixed plan
Your stack
CRM, helpdesk, WhatsApp, docs
Any model
OpenAI, Claude, Gemini, Sarvam
In production
Working software, not a demo
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The layer
The pieces we assemble after we have researched the workflow. The mix is scoped per engagement — not a catalogue you buy off the page.
Support with citations and hand-off
Routine questions resolve in-thread, in your tone and policy. Anything the corpus cannot support goes to a person with the transcript attached.
Lead qualification into the CRM
Hold a natural conversation, capture the fields sales actually uses, score the lead, and write hot ones into the CRM you already pay for.
Internal-ops agents with guardrails
Search, draft, summarise, look things up across systems. Routine actions can run with an audit log. High-stakes actions wait for a person.
Channels you already use
Website, WhatsApp, Instagram, email, Slack, in-app. One reasoning layer on the front doors you already pay for — not a new one to maintain.
The right model for the job
OpenAI, Claude, Gemini, or Sarvam — picked for the task. The product stays current as models improve.
02
Safe to put in front of people
What has to hold up once a real customer, or a real teammate, is on the other side of the thread.
Roles and least privilege
Control who can change prompts, knowledge, routing and integrations. Defaults are tight; access is named, not shared.
Audit trail
A reviewable record of retrieved sources, replies, queued actions, and who approved a high-stakes step.
In the inbox you already have
AI and human threads stay in the helpdesk or CRM the team already lives in — notes, assignment, and an audit log. No new product to log into.
Human hand-off when it matters
Intent, sentiment, or an SLA timer opens the thread for the right person — with the CRM or helpdesk record already loaded.
Data residency and retention
Choose where data lives and how long it is kept. We can build entirely on your infrastructure and region.
Your data stays yours
Your content is used to run the product — not to train someone else’s model. Export or delete on your terms.
03
Built to stay in production
A finished product your operators can run. Instrumented, versioned, and ready for the next workflow.
Versioned API surface
A stable interface for everything the layer does, so downstream systems are not coupled to a prompt.
Signed webhooks
Outbound events with signatures and delivery visibility, so the rest of the stack can react without polling.
Models that can move
OpenAI, Claude, Gemini, or Sarvam — picked for the task. The product stays current as models improve.
Evaluation harness
A test set and scoring loop for your task, so quality regressions show up before they reach customers.
Contrast
A bolt-on chatbot is not a layer.
If a platform already does this well enough, we will say so on the discovery call. When it does not, this is the difference.
Bolt-onFitted layer
A widget for every company
Scoped to how you already work
Guesses from memory
Retrieves your docs and cites them
Dumps a ticket overnight
Hands off with the record loaded
A chatbot you configure
A product fitted to your desk
FAQ
Common questions about a fitted layer
Is ClickToAutomate a SaaS product or a custom build?
Fitted software. ClickToAutomate is a research-led studio in India. We study how you already work, then ship a working product on the tools you already pay for. You run the desk with it. We stay on the system.
What does a fitted AI layer include?
Typically retrieval over your documents with citations, support automation with human hand-off, lead qualification written into your CRM, internal-ops agents with an audit log, and the channel you already use (web, WhatsApp, email, Slack). The mix is scoped per engagement after research — not a fixed catalogue.
Which models do you use?
The design is model-agnostic: OpenAI, Claude, Gemini, Sarvam, or open models. We pick for the task and keep the product swappable as models improve. There is always an off switch.
Will the AI take high-stakes actions on its own?
No. High-stakes actions — refunds, reships, clinical or policy exceptions — wait for a person. Routine actions can run with an audit log. Hand-off includes the transcript, intent, and the CRM or helpdesk record already loaded.
How do we start a fitted AI build?
Book a 30-minute discovery call. You leave with a written plan: scope, approach, and trade-offs. There is no obligation. If a platform already fits better, we will say so.
Want a layer like this on your stack?
Thirty minutes. You leave with a written plan — which of these pieces apply, what we would not build, and the trade-offs. No obligation.
We’ll tell you if a platform already fits better.
