IT & ITES / BPM businesses in India, and what technology actually fixes

SLA-Aware Process Agents

The problem. Too many repetitive back-office steps, handoffs and exception checks consume agent and analyst hours while clients still expect the same SLA at a lower ₹-per-seat cost.

What we build. We map high-volume processes such as ticket triage, email classification, case updates, approvals and quality-check preparation, then build agents and automations around the existing tools and escalation rules. Exceptions remain with the right team instead of disappearing into an unattended queue.

What changes. Routine work moves through the operation faster, fewer handoffs sit idle, and supervisors can see where human intervention is still required.

You probably need this if: Team leaders are copying information between CRM, ticketing, spreadsheets and client portals, while analysts spend more time updating cases than resolving them.

Delivered as Workflow Automation & AI Agents from ₹39,999 one-time

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Private Coding And Knowledge Copilot

The problem. Developers and support analysts lose time searching scattered runbooks, client documentation, old tickets, code repositories and internal answers.

What we build. We build a domain-tuned AI workspace that retrieves approved engineering and operations knowledge, explains relevant code or procedures, drafts responses and produces grounded answers with source references. Access is separated by client, account and role so one programme's information is not exposed to another.

What changes. Developers and analysts reach the right internal answer sooner, produce more consistent work and spend less time asking senior staff to repeat context.

You probably need this if: The same technical and process questions appear in Teams, Slack and ticket comments, and experienced employees are repeatedly pulled away to explain known procedures.

Delivered as Custom AI Build from ₹2,00,000 one-time

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Autonomous Regression Test Factory

The problem. Frequent releases create regression risk, while manual testing cannot keep pace across web applications, APIs, mobile flows and client-specific configurations.

What we build. We turn acceptance criteria, existing defects and critical user journeys into maintainable automated tests connected to the delivery pipeline. The setup covers risk-based regression suites, test data handling, failure evidence and clear ownership for triage.

What changes. Release teams receive earlier, reproducible evidence of what broke, with fewer critical paths dependent on last-minute manual checking.

You probably need this if: A release is delayed because testing is still running, or a defect reaches a client after a change that was considered low risk.

Delivered as QA & Test Automation from ₹49,999 one-time

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Multilingual Service Desk Agent

The problem. Support queues grow faster than the available agents, especially for repetitive status, access, password, appointment and account questions across shifts and languages.

What we build. We deploy voice and chat agents trained on approved service-desk knowledge to identify the request, complete eligible actions, provide status updates and create or route tickets with a usable transcript. Complex, sensitive or low-confidence cases are transferred to a human with the collected context intact.

What changes. The queue absorbs routine demand outside office hours, agents start escalated conversations with better context, and supervisors can identify recurring contact drivers.

You probably need this if: Night-shift queues carry the same basic questions into the morning, and agents spend the opening minutes of each call confirming details already available in the ticket.

Delivered as AI Voice Agents & Support Automation from ₹1,25,000 one-time

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Client-Isolated Sovereign AI Stack

The problem. Clients want AI-assisted delivery, but contract terms, Indian data-residency expectations and confidentiality rules restrict sending source code, tickets or customer records to shared public AI services.

What we build. We deploy selected models and retrieval components inside the required private cloud, data centre or client-controlled environment, with tenant isolation, access controls, logging and model-use policies. The design is matched to latency, workload and client security requirements rather than forcing every account onto one shared setup.

What changes. The delivery organisation can introduce AI into controlled client workflows with clearer data boundaries, audit evidence and a path for security review.

You probably need this if: Security teams block useful AI pilots, clients ask where prompts and documents are processed, or delivery leaders are relying on unapproved personal AI accounts.

Delivered as Private & Sovereign LLM Deployment from ₹1,50,000 one-time

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Specialist Delivery Pod

The problem. Talent churn leaves gaps in automation, QA, cloud and AI delivery just when a client programme or internal platform has a committed deadline.

What we build. We provide a focused pod of vetted engineers, QA specialists, designers or security staff who work inside the existing delivery model, with agreed responsibilities, documentation and handover milestones. The pod can stabilise a project while the permanent team hires, trains or backfills roles.

What changes. Critical work continues without making a single departing employee the only owner of a system, and the internal team receives documented, reviewable delivery rather than temporary firefighting.

You probably need this if: A resignation stalls a client commitment, senior engineers are covering several vacancies, or delivery managers are repeatedly moving people between accounts to keep SLAs intact.

Delivered as Dedicated Team & Staff Augmentation from ₹60,000 one-time

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How do I know if we need sla-aware process agents?

You probably need this if Team leaders are copying information between CRM, ticketing, spreadsheets and client portals, while analysts spend more time updating cases than resolving them. Once it is running, routine work moves through the operation faster, fewer handoffs sit idle, and supervisors can see where human intervention is still required.

How do I know if we need private coding and knowledge copilot?

You probably need this if The same technical and process questions appear in Teams, Slack and ticket comments, and experienced employees are repeatedly pulled away to explain known procedures. Once it is running, developers and analysts reach the right internal answer sooner, produce more consistent work and spend less time asking senior staff to repeat context.

How do I know if we need autonomous regression test factory?

You probably need this if A release is delayed because testing is still running, or a defect reaches a client after a change that was considered low risk. Once it is running, release teams receive earlier, reproducible evidence of what broke, with fewer critical paths dependent on last-minute manual checking.

How do I know if we need multilingual service desk agent?

You probably need this if Night-shift queues carry the same basic questions into the morning, and agents spend the opening minutes of each call confirming details already available in the ticket. Once it is running, the queue absorbs routine demand outside office hours, agents start escalated conversations with better context, and supervisors can identify recurring contact drivers.

How do I know if we need client-isolated sovereign ai stack?

You probably need this if Security teams block useful AI pilots, clients ask where prompts and documents are processed, or delivery leaders are relying on unapproved personal AI accounts. Once it is running, the delivery organisation can introduce AI into controlled client workflows with clearer data boundaries, audit evidence and a path for security review.

How do I know if we need specialist delivery pod?

You probably need this if A resignation stalls a client commitment, senior engineers are covering several vacancies, or delivery managers are repeatedly moving people between accounts to keep SLAs intact. Once it is running, critical work continues without making a single departing employee the only owner of a system, and the internal team receives documented, reviewable delivery rather than temporary firefighting.