As Agentic AI Development Services serving Pune, we build agents that read the situation, use the tools already in place, and make the judgment call inside your actual systems instead of stalling on the unfamiliar case. Ownership of every credential and every piece of infrastructure sits with you from day one, so nothing about running this depends on us sticking around after handover.
Start a Discovery
80+
Workflows Automated
60+
Engineers In-House
96%
Client Retention
Built for teams at a specific inflection point.
From a single scoped agent to a coordinated multi-agent platform, we design, build, and operate across the full spectrum. Start where the value is clearest and expand from there.
A single agent, built around a single workflow: a defined boundary for what it can decide on its own, the exact tools it is permitted to call, and a fallback that catches everything else.
Single-agentMulti-step reasoningTool use
A single agent, built around a single workflow: a defined boundary for what it can decide on its own, the exact tools it is permitted to call, and a fallback that catches everything else. Nothing reaches a live queue until it has been proven against your own documents and data first.
Not every process suits an agent, and this step settles that honestly.
FeasibilityArchitectureRoadmap
Not every process suits an agent, and this step settles that honestly. Whether agentic AI development services fit a particular workflow gets assessed here first, before any build commitment is made.
Engineering, procurement, and finance data rarely sit in one place, so a single agent covering all of it is uncommon.
Agent orchestrationLangGraphCrewAI
Engineering, procurement, and finance data rarely sit in one place, so a single agent covering all of it is uncommon. This pillar is the orchestration layer connecting several agents: how they hand work to each other, share context, and escalate when needed, in a way that stays traceable end to end.
Instructions get read, variation gets handled, failures get retried automatically, and a person only gets pulled in where a real decision is required.
Full workflow executionEvent-drivenHuman-in-the-loop
Instructions get read, variation gets handled, failures get retried automatically, and a person only gets pulled in where a real decision is required. The agent takes over the process itself, not a sanitised version of it.
An agent that cannot reach your systems only exists on paper.
REST & webhookCRMERPLegacy connectors
An agent that cannot reach your systems only exists on paper. This pillar covers wiring it into CRM, ERP, and the legacy platforms common across Pune's product engineering and delivery teams, with the integration scoped out well before sprint work begins.
Answers trace back to actual policies, specifications, and documentation, each one carrying a citation.
Vector storesRetrieval pipelinesGrounded outputs
Answers trace back to actual policies, specifications, and documentation, each one carrying a citation. That is what separates output a team can verify from output they just have to trust.
What we have built, across categories.
What has actually shipped, sorted by category.
Where global delivery teams lose the most time to coordination.
Intake requests, gather context across systems, route to the right queue, and resolve routine cases without a handoff.
Read undocumented data formats, map fields against modern schemas, and flag inconsistencies before a migration goes live.
Pull delivery metrics across tools, generate status reports on schedule, and flag risk before it becomes a missed commitment.
Where design and testing data meet decisions that used to need an engineer.
Extract specifications and test data from design documents, check them against standards, and flag deviations for review.
Gather qualification documentation, check it against criteria, and flag gaps before a component is approved.
Pull results from multiple test rigs, match them against expected values, and flag anomalies for engineering review.
Where the hours a team loses to coordination get handed back.
Open invoices, forms, and claims, pull the fields out, check them against a rule set, and route or flag whatever needs a second look.
Gather context, apply routing logic, track deadlines, and bring in a person only when a real call needs making.
Pull records from more than one system, match them, flag what does not line up, and post the result to the right place.
Shaped around how each industry here actually works. regulated BFSI.
How we build, every step of the way.
The engineering discipline behind every build.
The boundary comes first: what the agent is trusted to decide alone, what gets kicked upstairs, and what gets logged regardless of outcome. That definition, plus a confidence threshold and a clear stop point, exists before a single line of code gets written.
The target number gets agreed on using a test set from your own documents, and that happens before production code exists, not once the agent is already running.
Every connector reports its retry counts and error rate into one place your team can look at directly. No digging through logs that only the people who wrote them can interpret.
Checks and review triggers run alongside the agent from the start, so if accuracy starts slipping, that shows up on our side before a delivery team or end user has to point it out.
Why teams pick us for this.
A framework does not get picked until the workflow itself is understood. That ordering is deliberate: the architecture follows what is actually found, so the agent is built right the first time rather than reworked partway through.
Every credential, every line of prompt logic, and the vector store itself live in accounts that belong to you, built on open foundations. Nothing about running, changing, or handing this off to a different team depends on us staying in the picture.
An accuracy benchmark, a defined fallback, and monitoring exist from the day the agent ships, which means performance is visible internally long before a customer or delivery lead would have reason to ask.
There is no rotation between the people scoping the work and the people building it, they are the same people throughout, whether the engagement sits inside one pillar or draws on the wider AI development practice behind it. Nobody hands this off to a junior bench after the first call.
Four phases take this from a mapped process to a live agent, on a timeline that holds.
Book a call{ 01 }· 1 to 2 weeks
Every input, output, decision point, and system touchpoint gets documented, in line with how AI development services get scoped elsewhere in this practice. This phase ends with a ranked scope, a reference architecture, and a timeline that does not shift later.
{ 02 }· two-week sprints
Extraction logic, reasoning, integrations, and the monitoring layer come together in short sprints, each closing with a working demo on staging that mirrors production closely. Teams with established engineering practices sometimes compare this rhythm against their own CI/CD pipeline before deciding how the two should connect.
{ 03 }· before go-live
Real data runs through the agent, results get measured against what was agreed earlier, and every edge case gets resolved before production traffic ever touches it.
{ 04 }· launch and ongoing
Rollout happens in stages, monitoring stays active throughout, and a proper handover follows, with tuning afterward based on what actually shows up once the agent is live.
Ways to work
Two ways to work with us. Whichever model fits, Agentic AI Development Services in Pune start with the same senior engineers from week one.
One defined workflow, a fixed list of integrations, and acceptance criteria settled before anything starts. The price is fixed, the timeline holds, and the finished system belongs to you outright, a reasonable way to try agentic AI solutions in Pune on a single workflow before committing further.
A senior pod embedded in your existing tools, running two-week sprints across whatever builds and new candidates come up. This shape tends to suit teams once the first agent is already live, particularly GCC teams juggling several workflows at once.
Questions, answered.
An agent plans across several steps, calls outside tools, and makes decisions based on context to reach a goal without a person guiding each step. A chatbot only answers a single question; an agent runs the process itself, exceptions included.
The workflow gets assessed before a framework gets picked, and the same senior engineers stay on from the first assessment through handover, with no midway handoff to a different team.
A single-agent build typically runs four to eight weeks from assessment to production. A multi-agent system takes eight to sixteen weeks, and the assessment phase gives a firm number before any commitment is made.
Yes. Agents are commonly built to read undocumented legacy formats, map data against modern schemas, and flag inconsistencies during a migration, a frequent need for Pune's enterprise IT and digital engineering firms undertaking modernisation work.
Yes. Global in-house delivery centres typically need agents that plug into existing ticketing, reporting, and delivery tools without disrupting processes already in place. We scope these builds around what the centre's parent organisation already runs elsewhere, so the agent fits into a global operating model rather than sitting apart from it.
The client does, entirely. Agent definitions, prompt logic, vector stores, credentials, and cloud infrastructure all sit in the client's own accounts, on foundations a team can run and change without needing outside help. We apply the same approach in our Agentic AI development services in Ahmedabad.
Tell us the process, the volume, and where you want to go further. A senior AI engineer replies within one working day. Direct conversation, real answers, a real plan.
Step 1 - Tell us where you are
Which workflow costs you the most time or carries the most risk. We sign an NDA before any specifics.
Step 2 - Speak to an agent engineer
A senior AI engineer joins within two working days to map your process, your integration landscape, and the shortest path to a working agent.
Step 3 - Get a real plan
A workflow architecture, a scope band, and an accuracy benchmark you can plan against, plus a production system built to last.
Tell us the process, the volume, and where you want to go further. A senior AI engineer replies within one working day. Direct conversation, real answers, a real plan.
