As Agentic AI Development Services serving Kolkata, we build agents that work through the moments that don't fit a template, planning across the task and acting inside your real systems instead of pausing at the first thing that does not fit a template. Every credential and every piece of infrastructure stays under your own control, so none of this depends on us once the build is handed over.
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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.
Each engagement starts narrow: one agent, built around one workflow, with a hard limit on what it can decide by itself and a defined escalation path for everything past that limit.
Single-agentMulti-step reasoningTool use
Each engagement starts narrow: one agent, built around one workflow, with a hard limit on what it can decide by itself and a defined escalation path for everything past that limit. The build gets proven against your own data before it ever touches a live queue.
Some processes are not ready for an agent, and saying so honestly is the whole point of this step.
FeasibilityArchitectureRoadmap
Some processes are not ready for an agent, and saying so honestly is the whole point of this step. Whether agentic AI development services suit a particular workflow gets settled here, before any commitment to build.
Trade, logistics, and finance records rarely live in one system, so a process that spans all three usually needs several agents working in coordination.
Agent orchestrationLangGraphCrewAI
Trade, logistics, and finance records rarely live in one system, so a process that spans all three usually needs several agents working in coordination. This pillar is that coordination layer: handoffs, shared state, and escalation rules that keep the whole thing auditable.
Instructions get read, variation gets handled, failures get retried on their own, and a person only steps in where a genuine decision is required.
Full workflow executionEvent-drivenHuman-in-the-loop
Instructions get read, variation gets handled, failures get retried on their own, and a person only steps in where a genuine decision is required. The agent takes on the process itself, not a simplified version of it.
An agent that cannot reach a system is only useful on paper.
REST & webhookCRMERPLegacy connectors
An agent that cannot reach a system is only useful on paper. This pillar covers connecting it to CRM, ERP, and the legacy platforms still running across Kolkata's trading houses, manufacturing exporters, and financial institutions, with the integration mapped out well before any sprint starts.
Answers trace back to real contracts, policies, and internal records, each carrying its own citation.
Vector storesRetrieval pipelinesGrounded outputs
Answers trace back to real contracts, policies, and internal records, each carrying its own citation. That is the line between an answer a team can verify and one they simply have to accept.
What we have built, across categories.
What has actually shipped, sorted by category.
Where contract terms and shipment data meet decisions that used to need a trader's judgment.
Match trade contracts against delivery notes and invoices, flagging discrepancies in quantity, grade, or price before settlement.
Pull inspection and grading data, check it against contract specifications, and flag deviations for review.
Track payment status against contract terms, flagging overdue settlements and reconciling ledgers across counterparties.
Where export paperwork used to mean hours of manual cross-checking.
Assemble shipping and customs paperwork against order details, flagging mismatches before a shipment is booked.
Track compliance certifications, testing results, and renewal dates across a supplier and buyer base.
Match purchase orders against production and inventory data, flagging discrepancies before they become shipment problems.
Where ticketing and back-office queues lose the most time to manual routing.
Intake requests, gather context across systems, route to the right queue, and resolve routine cases without a handoff.
Intake claims or applications, verify supporting documents, and route to the right team with a summarised case file attached.
Pull data across systems, generate reports on schedule, and flag anomalies before a deadline.
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 line gets drawn before any build work starts: what the agent handles alone, what gets kicked upstairs, and what gets logged no matter the outcome. A confidence threshold and a defined stop point are part of that line, not an afterthought.
The number the agent has to hit gets settled against a sample from your own transaction or document data, and that happens before any production code exists.
Every connector surfaces its own retry counts and error rates into a place your team can look at directly, instead of leaving that information sitting in logs only engineers can read.
Review checks run in parallel with the agent from day one, so a slip in accuracy shows up internally long before an operations lead or customer would need to raise it.
Why teams pick us for this.
Nobody picks a framework before the workflow itself is understood, and that ordering is deliberate. It means the agent gets shaped around what was actually found, rather than getting bent afterward to fit a tool chosen too soon.
Prompt logic, credentials, and the vector store live in accounts under your control, built on foundations that belong to no one else. Running this, changing it, or moving it to a different team never has to go through us.
A benchmark, a fallback, and monitoring all exist starting the day the agent ships, so how it is performing stays visible to your team long before a customer would have reason to notice a problem.
The person who scopes the workflow is the same one who builds it, from the first call to the last sprint, regardless of whether the work touches one pillar or spans AI development more broadly. Nobody rotates in a junior team midway through.
The work moves through four phases, from a mapped process to a live agent, on a schedule that actually holds.
Book a call{ 01 }· 1 to 2 weeks
Inputs, outputs, decision points, and system connections all get written down first; the same groundwork any AI development services engagement needs before a build starts. The output is a ranked scope, a reference architecture, and a timeline that stays fixed.
{ 02 }· two-week sprints
Extraction logic, reasoning, integrations, and monitoring come together across short sprints, each one closing with a working demo on a staging setup that mirrors production closely.
{ 03 }· before go-live
Real data runs through the agent, results get measured against the benchmark set earlier, and every edge case gets closed out before production traffic ever touches it.
{ 04 }· launch and ongoing
The rollout happens in stages with monitoring active the whole time, followed by a proper handover and tuning based on what production actually shows once it is live.
Ways to work
Two ways to bring us in. Either one starts with the same senior engineers handling Agentic AI Development Services in Kolkata from week one.
One workflow, a fixed list of integrations, and acceptance criteria settled before work begins. The price stays fixed, the timeline stays fixed, and the finished system belongs to you outright, a reasonable way to try agentic AI solutions in Kolkata on a single process before committing further.
A senior pod embedded in your existing tools, running two-week sprints across whatever builds and new candidates surface next. This shape tends to suit teams once the first agent is already live and a trading, export, or finance function keeps finding more work to hand off.
Questions, answered.
An agent works through several steps on its own, calls outside tools, and makes decisions based on context to reach a goal without a person guiding each step. A chatbot answers a single question; an agent carries 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 match trade contracts against delivery notes and invoices, check quality grading data against contract terms, and track settlement status across counterparties, built around the specific contract structures a trading house already uses.
Yes. Agents assemble shipping and customs paperwork against order details, track certification and testing renewal dates, and flag discrepancies before a shipment is booked, matched to the compliance standards export buyers expect.
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 Mumbai.
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.
