AI agents and workflows
Most businesses do not need a clever agent. They need one slow, repetitive task taken off someone's desk, safely. We find which tasks are worth automating, design the workflow, build it with the right model and make sure a person can step in where it matters. Sometimes the right answer is a plain automation, and we will say so.
From the first workflow map to an agent you can trust
Find the right tasks
We map how work moves today and pick the steps where AI saves real time. Not everything should be an agent, and we tell you which parts should not.
Workflow and agent design
What the agent decides, which tools it may use, what it remembers and when it must stop. Fixed steps stay in ordinary code and only the judgement goes to the model.
Connected to your tools
WhatsApp, email, your CRM or sheets, your own database and internal tools, so the agent works where your team already works.
Answers from your own facts
Agents answer from your documents, prices and policies, and say so when they do not know instead of guessing.
People in the loop
Approvals for risky actions, clear permissions and a log of what the agent did. A person can always step in.
Tested, watched and costed
We test on real examples before launch, then watch quality and cost after it and tune the agent as your work changes.
Is this you?
Teams answering the same questions
Price, availability, order status, documents needed. An agent can answer the routine ones from your own facts and pass the rest to a person.
Work that moves between tools
Copying details from WhatsApp or email into a sheet or CRM, chasing approvals, sending reminders.
Products that want an AI feature
You have an app or platform and want one smart step inside it, done safely.
Four steps, no surprises
Pick one task
We sit with the people doing the work and choose one task that is slow, repetitive and easy to check.
Design the flow
Which steps stay plain code, which need the model, what it may touch and when a person approves.
Test on real examples
We run it on your past cases before anyone relies on it and count how often it gets them right.
Start small, then widen
A few users or a share of the work first. We watch quality and cost, then give it more.
What it can include
- AI models from OpenAI, Anthropic and Google
- Instagram, WhatsApp and email
- Connections to Zapier, Make and your CRM
- Spending limits and a log of every action
- Backend and database (Node.js, PostgreSQL)
We're not the right fit if…
- You want to "add AI" without a task in mind. We start from a job to be done, not from a model.
AI replies for Instagram DMs
Sellers using Dhriyo get DMs like "price?" and "is this available?" that keyword rules miss. We added an AI reply step, and had to make it safe to run on a real business's account.
One job for the model
The AI writes a short plain-text reply. Who gets a message, when and how many stay in ordinary code. We dropped an extra classification step after it caused wrong matches, and the flow got simpler and more reliable.
Grounded in the seller's own facts
Replies are based on the business info the seller writes down: prices, hours, links. If the answer is not there, the AI says it will connect the customer with the owner instead of guessing.
Guardrails in code
Plain text only, no invented links, a length cap and a timeout, so a slow or odd reply never blocks a conversation.
Cost and choice under control
An hourly AI budget per workspace stops runaway spend when a post goes viral. Each seller picks their model provider and uses their own key, stored encrypted.
Still works when AI does not
If the AI call fails or the budget is used up, the normal keyword replies keep flowing. The AI client and credentials are covered by tests.
This is the shape of most agent work: a narrow task, real data to stand on, limits written in code and a safe fallback.
Before you call
What is an agentic workflow?
A task done in several steps where an AI model decides some of the steps, uses tools like your database or WhatsApp and hands over to a person when it should. The other steps are ordinary software.
Do I need an agent for this?
Often not. If the steps never change, a plain automation is cheaper and more reliable. We tell you which parts need AI and which do not.
Which AI models do you use?
We choose per job from the main providers, such as OpenAI, Anthropic and Google, and build it so you can switch later.
Is our data safe?
We send the model only what the task needs, keep keys encrypted and log what the agent does. Where data has to stay inside your own systems, we design for that from the start.
What does it cost to run?
The model provider bills by use. We estimate it from your volumes before we build, put a spending limit in the code and show you the spend.
How do we know it's working?
We agree a few measures before launch: how many cases it handles, how often a person has to correct it and what it costs. You see those numbers after launch, not just a demo.