Customer service agent.
Resolves tickets end to end: reads the case, checks the order, issues a refund within policy and replies.
── AI agent development services ──
AI agents Development.
Agents that plan, call your tools and finish multi-step work across your systems, inside limits you set, with a person approving anything that matters.
Free 30-minute scoping call. Written estimate within 5 working days.
Allow-listed tools · Approval gates · Replayable traces · Your cloud
Illustrative interface. Values are sample data.
By the numbers
0+.AI systems in production
0.Days to first deploy · median
0.0%.Uptime · managed inference · 12 mo
0.0×.Cost reduction · fine-tuned vs API · median
* Sample figures for layout review. Replace with audited numbers from engagement reports before launch.
── 01 · AI agent development services ──
Eight services, from idea to managed agent.
AI agent development is building software that plans and completes multi-step work on its own, safely enough to trust with your systems.
-
01
Agent strategy.
Which workflows suit an agent, how much autonomy each should have, and the business case behind it.
-
02
Agent design and development.
Goals, tools, memory and prompts, designed and built for one workflow at a time.
-
03
Conversational agents.
Agents your customers and staff talk to, on chat, voice, Slack or Teams.
-
04
Tool and system integration.
Allow-listed connections to your CRM, ERP, ticketing and internal APIs.
-
05
Agent testing.
Scenario test sets, simulated tools and red-teaming before any release.
-
06
Agent optimisation.
Fewer steps, cheaper models and faster runs, measured against the same tests.
-
07
Lifecycle management.
Monitoring, trace reviews, model upgrades and new tools, month after month.
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08
Managed agents.
We run the agent for you, in your cloud, with monitoring and on-call.
── 02 · How an agent works ──
A loop, six parts and a person.
Every agent we build runs the same loop, and every part of it is something you can inspect, limit and test.
-
Model
Any modelReasons about the goal and picks the next step. Chosen per task on your evaluations.
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Tools
Allow-listedThe APIs the agent may call, each with its own permissions and limits.
-
Memory
Task and long-termWhat has happened in this task so far, and what it learned from earlier ones.
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Knowledge
RetrievalYour documents and records, fetched with the same permissions a person would have.
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Guardrails
Every stepPolicy checks, spend limits and a step budget on every run.
-
Human approval
Where it mattersGates on any action that spends money, sends a message or changes a record.
── 03 · Agents we build ──
Five agents teams ask for most.
Each one works inside a single workflow first, with a clear goal, a short list of tools and a person on the risky steps.
Sales operations agent.
Enriches new leads, updates the CRM and drafts the follow-up after every call.
Finance operations agent.
Matches invoices to purchase orders, flags variances and posts to the ledger after approval.
IT helpdesk agent.
Handles access requests, triages incidents and pulls the right runbook steps into the ticket.
Research and analysis agent.
Searches documents and data, compares sources and writes a cited brief for review.
* Trace shown is illustrative sample data.
── 04 · Levels of autonomy ──
Autonomy is earned, one level at a time.
Most agents start at level 2. They move up only when the approval data shows they get it right.
-
Level 1, Assistant: It suggests; people act.
The agent finds information and proposes the next step. A person does everything.
- — The person's role —
- Does the work, with better information.
- Research and drafting
- New or sensitive workflows
- Building trust in the data
-
Level 2, Copilot: It drafts; people approve.
The agent prepares each action in full. A person approves it before anything happens.
- — The person's role —
- Approves every action.
- Replies, updates and form filling
- Customer-facing work at first
- Collecting approval data
-
Level 3, Workflow agent: It acts within rules.
The agent completes the task itself inside agreed limits, and asks only when a threshold is crossed.
- — The person's role —
- Approves only what crosses a limit.
- Refunds under a set amount
- Invoice matching
- Access requests
-
Level 4, Autonomous: It runs end to end, audited.
The agent handles the whole workflow. People review samples, exceptions and the weekly report.
- — The person's role —
- Reviews samples and exceptions.
- High-volume, low-risk tasks
- Well-measured workflows
- Mature evaluation sets
Not sure which level fits? On the scoping call we map your workflow's risk and volume to a starting level, and the evidence needed to move up.
Plan an agent
── 05 · Controls ──
Agents on a short leash.
An agent that can act can also make mistakes at speed. These five controls ship with the first version.
-
01
Allow-listed tools.
An agent can only call the tools it has been given, each scoped to the smallest permission that works.
-
02
Approval gates.
Actions that spend money, send messages or change records wait for a person until the data says they needn't.
-
03
Budgets and limits.
Every run has a cap on steps, spend and time. Hitting a cap stops the run and alerts a person.
-
04
Replayable traces.
Every plan, tool call and result is logged, so any run can be replayed for debugging or audit.
-
05
Tested before release.
Scenario tests with simulated tools run on every change. A regression blocks the deploy.
Illustrative interface. Values are sample data.
— Case studies —
The first cohort is being written.
Named case studies publish here with the client's sign-off, real numbers and the honest limits. Want to be one of them?
Talk to us →
── 06 · Agents, RPA and chatbots ──
When an agent is the right tool.
Agents are not always the answer. If the steps never change, RPA is cheaper; if people only ask questions, a chatbot is enough.
| Compared on | Chatbot | RPA bot | AI agent |
|---|---|---|---|
| What it does | Answers questions | Repeats fixed clicks and keystrokes | Plans and completes multi-step work |
| When the input changes | Hands over to a person | Breaks until someone fixes it | Adapts, or asks a person |
| Takes actions | Rarely | Yes, fixed steps only | Yes, through allow-listed tools |
| Explains what it did | A transcript | A log of steps | A replayable trace with reasons |
| Best for | FAQs and simple requests | Stable, rule-based screens | Variable work across several systems |
── 07 · How we build agents ──
From one workflow to a working agent.
Scenario tests come before the agent, and a demo every Friday shows it passing more of them.
-
01
Discovery · week 1
Workflow and data audit.
We map the process, pull a sample of real data, and check what is usable, missing or sensitive.
-
02
Discovery · week 2
Evaluation set.
We build the test set from your real examples, so “done” has a number before any product code exists.
-
03
Build · sprint 1
Architecture and prototype.
Model choice, retrieval design and integration plan, proven against the eval set.
-
04
Build · every Friday
Build in the open.
Your repository, your cloud. A weekly demo with accuracy, latency and cost on one screen.
-
05
Run · go-live
Deploy.
A staged rollout behind a feature flag, with runbooks, monitoring and a rollback path.
-
06
Run · monthly
Monitor and improve.
Drift alerts, model upgrades run through your evals, and a monthly cost and quality review.
── 08 · Technology stack ──
What our agents run on.
Any model, orchestration that stays readable, and tracing on every call.
— Models —
OpenAI
Anthropic
Gemini
Llama
Mistral
Qwen
— Agents & orchestration —
LangChain
Temporal
FastAPI
Python
— Retrieval & data —
pgvector
Pinecone
Elasticsearch
Redis
Databricks
Snowflake
— Training & serving —
PyTorch
Hugging Face
vLLM
NVIDIA TensorRT
ONNX
OpenCV
— MLOps & observability —
MLflow
Weights & Biases
Grafana
Prometheus
OpenTelemetry
— Cloud & infrastructure —
AWS
Google Cloud
Azure
Kubernetes
Terraform
Docker
* Tools we build with. No vendor partnership or endorsement is implied.
── 09 · FAQ ──
AI agent questions, answered.
Short answers here. Longer ones on the scoping call.
— Still have a question? —
Message us on WhatsApp. An engineer replies within one working day.
What is an AI agent?
An AI agent is software that uses a language model to plan and complete a multi-step task: it decides the next step, calls tools such as your CRM or ERP, checks the result and continues until the goal is met or a person is needed. “Agentic AI” is the broader term for systems built this way.
How much does it cost to build an AI agent?
It depends on the workflow, the number of tools the agent uses and the level of autonomy. Projects start with a fixed-fee discovery sprint, quoted on the scoping call; the build is priced per milestone, and every quote is written and fixed for its scope.
How long does it take to develop an AI agent?
After a two-week discovery sprint, a first agent on one workflow usually takes six to twelve weeks, including integrations and testing. Most start at copilot level, with a person approving actions, and gain autonomy as the approval data builds up.
How are AI agents different from RPA and chatbots?
A chatbot answers questions. RPA repeats fixed steps and breaks when a screen changes. An agent plans its own steps toward a goal, uses your systems through their APIs, and adapts or asks a person when something unexpected happens.
Can AI agents work with our existing tools?
Yes. Agents act through the APIs of the systems you already use, such as your CRM, ERP, helpdesk, email and internal services, with each tool allow-listed and its permissions kept to the minimum.
How do you stop an agent from doing something it shouldn't?
Four layers: the agent can only call allow-listed tools; risky actions need a person's approval; every run has limits on steps, spend and time; and every action is logged in a trace you can replay.
How do you test an AI agent before release?
We build scenario tests from real cases, run them against simulated versions of your tools, and add adversarial cases that try to push the agent off course. Every change to prompts, models or tools runs the full suite before it ships.
Which models and frameworks do you use?
We are model-agnostic and choose per task on your evaluations. Agents are built with frameworks such as LangGraph or in plain code, with the Model Context Protocol where it helps connect tools.
── Start a project ──
Bring the workflow. We'll bring the plan.
Three steps from first message to kickoff. No deck, no commitment until you sign.
- 01Scoping call30 minutes with an engineer, not a salesperson.
- 02Written estimateAn evaluation plan, a timeline and a price within 5 working days.
- 03KickoffSign the scope and the discovery sprint starts.
Prefer to message directly? WhatsApp +91 7096010005
