What is a Nano GCC?
"A small, AI-first capability hub of 10 specialists that delivers the output of a 50–100 person traditional team."
Every team member is amplified by Swaran Soft's agentic AI platform — taking on the repeatable load so humans focus on judgement, design and exception-handling. It launches in 4–6 weeks, runs on a fixed monthly fee, and can transfer to your captive ownership at month 24 (BOOT).
This isn't outsourcing. It's not staff augmentation. It's not a traditional GCC. It's a new operating model purpose-built for the mid-market enterprise that wants frontier AI capability without enterprise-scale overhead.
AI-First Delivery
Agents handle 30–60% of repeatable workload from day one. Humans focus on what compounds.
Lean Core Team
Ten specialists. Cross-functional. Each one accountable for an outcome, not a ticket.
4–6 Week Setup
From discovery to first agent in production. Compared to 12–18 months for a captive.
BOOT Optionality
Take captive ownership of the team and entity at month 24. Or stay managed. Your call.
Why the traditional GCC model is breaking.
Two decades of "build a 500-seat captive" was designed for the Fortune 500 and Year-1 cost arbitrage. Your business is neither. Here is the honest side-by-side.
The captive model that no longer fits the mid-market
An AI-first capability hub that ships in weeks, not years
The Core 10 that powers your AI GCC.
Ten roles. Cross-functional. Every one of them amplified by our agentic platform — so the team's effective throughput sits at 50–100 person equivalent.
AI Architect
Designs the agent topology, model strategy, and orchestration layer for your domain.
Agent Orchestration Engineer
Builds multi-agent pipelines, hand-offs, memory and tool-calling infrastructure.
Automation Workflow Engineer
Wires legacy systems and SaaS tools into the agent layer using n8n, APIs and ETL.
Data Engineer
Owns the data pipeline, vector stores, RAG infrastructure and governance plumbing.
QA & AI Testing Specialist
Builds eval harnesses, regression suites and red-team frameworks for agent reliability.
DevOps / Cloud Engineer
Runs the deployment pipeline across cloud, hybrid and on-prem GB10 infrastructure.
Prompt Engineer
Designs and tunes the prompt layer, instruction sets and multi-step reasoning patterns.
UI / UX Engineer
Ships the human-in-the-loop interfaces, dashboards, and exception-handling consoles.
Business Analyst
Translates your business outcomes into agent-ready workflows and acceptance criteria.
GCC Program Manager
Single accountable owner. Weekly delivery cadence. SLA, governance and your escalation path.
These ten roles, augmented by AI agents running on Claude, GPT-4o, Sarvam, BharatGen and Krutrim — orchestrated under one governance layer — deliver the output of a 50–100 member traditional team. That is the multiplier.
From discovery to production-grade in 4–6 weeks.
Five phases. Fixed timeline. No ambiguity. Each phase has named deliverables and owner-acceptance gates.
Use Case Identification
Workshop your operations. Map repeatable workloads. Pick the highest-leverage agent targets.
Agent Design & Architecture
Topology, model selection, governance posture, data residency, integration map.
Workflow Automation Setup
Build the agent pipelines. Wire your systems. Stand up evaluation and observability.
Pilot Deployment
First agents in production behind human oversight. Weekly delivery review with you.
Scale with AI Agents
Expand the agent library. Re-engineer adjacent workflows. Compound the multiplier.
Built for scale. Built in Gurugram.
Your Nano GCC operates from Swaran Soft's flagship AI Delivery Hub in Gurugram — engineered for enterprise-grade security, scalable to 150,000 sq ft as your operations grow.
This is not a vendor desk lease. It is your long-term AI capability centre. ISO 9001 certified. SOC 2 Type I underway. DPDP and GDPR-compliant data handling. Hybrid hosting across cloud VPC, on-premises (Dell Pro Max GB10) and air-gapped configurations as your governance posture demands.
25+ years of engineering excellence — now AI-first.
Swaran Soft has been building enterprise software for over two decades. Across automotive OEMs, electronics conglomerates, energy majors, healthcare networks and metro rail corporations — we have shipped to production at enterprise scale long before the agentic AI era began.
What changes in 2025–26 is not whether we deliver — it is how much we deliver per engineer. The Nano GCC is the operating model that lets a 25-year-old engineering firm move at a 25-month-old AI-native firm's velocity, with all the governance maturity intact.
When you build a Nano GCC with us, you get a partner that has shipped to production at enterprise scale long before the agentic AI era began — and a team that has rebuilt itself, top to bottom, around AI-first delivery.
Why enterprises are moving to the Nano GCC model.
Procurement-grade ROI math. Each number below holds up under scrutiny when your CFO opens the spreadsheet.
Cost-efficient vs Traditional GCC
10-person AI-multiplied pod costs ~30% of a 50-person traditional offshore team for equivalent throughput.
Cost-efficient vs Western In-house
Forge-tier all-in monthly is less than two senior US AI engineers fully loaded.
To first agent in production
Compared to 12–18 months for a captive build. Your roadmap moves this quarter, not next year.
Entity setup & capex
No MoA. No real estate lease. No recruiter retainer. Single fixed monthly fee, all-inclusive.
Calculate your savings.
Drag the slider. Pick your region. See the annual delta against your current model. Conservative procurement-grade estimates — not marketing math.
Real outcomes, across real industries.
Anonymised at the client's request — but the operational gains are exactly as measured. Every story below is a Nano GCC pod or its precursor in production today.
From 6-week QA cycles to 4× release cadence
Manual QA cycles taking 6–8 weeks per release across infotainment, vehicle inspection and dealer apps. Engineering team blocked on regression.
10-person pod with AI test agents running automated regression, visual diffing and exploratory testing. Human QA leads focus on judgement calls.
From multilingual ticket backlog to 9-language auto-resolution
Multilingual support load across 9+ languages. Rising ticket volume on field service and partner queries. L1 agents spending hours on routine triage.
AI agent layer for first-line triage + 10-person Nano GCC running L2/L3 escalations. Sarvam and BharatGen models for Indic-language coverage.
From 6-month data pipelines to 8-week production deployment
Data engineering scattered across 30+ business units. Slow time-to-insight. Each business unit hiring its own data team — duplicated cost, inconsistent governance.
Centralised Nano GCC pod running agent-driven ETL pipelines, data quality automation and self-serve analytics. Replaces a planned 50-person captive data team.
From cloud cost overruns to sovereign-grade DevOps velocity
Cloud cost overruns of 35–50% across business units. Slow deployment cycles in a regulated environment. Hard data-residency requirement — no public cloud.
AI-driven DevOps Nano GCC operating with hybrid Dell GB10 on-prem + sovereign-region cloud. Agent-led FinOps, automated deployment and observability.
Frequently Asked Questions
Ready to build your Nano GCC?
Tell us a little about your operations. Within 24 hours, our AI GCC specialist will walk you through what a 10-person Nano GCC looks like for your specific use case — with indicative pricing, a proposed pod composition, and a 4–6 week build plan.
Book your 1:1 meeting
An AI GCC specialist will respond within 24 hours.