Data & AI services for capability centres — advise, build, transform, run.
SCIKIQ teams set your data and AI strategy, build the platforms, transform IT operations, security, finance and HR services, and run what we build — for global capability centres, IT and BPM service providers and enterprise shared services in India. Our SCIKIQ Data Fabric, pre-built accelerators and a supervised digital workforce of AI agents are how we deliver it faster.
Capability-centre data and AI programmes stall because every one starts from zero.
Tools inherited from every business unit, knowledge held in people’s heads and long implementations mean value arrives late — or not at all.
Patterns we found in delivered IT operations and shared-services work — not industry statistics.
1 · No governed data foundation
ITSM, CMDB, monitoring, ERP and HRMS don’t agree on services, owners or people — and lineage is unknown.
2 · Services firms rebuild from scratch
Each project re-invents pipelines, models and controls, so timelines and fees grow.
3 · Software alone doesn't change the process
Point products solve one use case and leave integration, data and adoption to the centre.
4 · AI pilots never reach production
Proofs of concept built without data, controls and an operating model stay on the shelf.
Capability centres are moving from dashboards and copilots to agents that triage, provision and test — under supervision.
Agents now read the queue, correlate the alerts, prepare the access grant and test the controls; a named person approves anything that changes a system.
So what for a capability centre: the operating model moves from “a person works the queue” to “agents do the work, a person supervises many agents” — which makes approvals, autonomy limits and audit trails the deciding capabilities, especially where access, production systems and ledgers are involved.
Counts from platforms as built; capabilities, not client results.
A platform that arrives with industry IP and the team to run it — so capability centres pay for outcomes, not reinvention.
Services
Nine service lines across advise, build, transform and run — delivered by teams who know IT service management, security and controls, and finance and HR shared services.
SCIKIQ Data Fabric
The governed foundation: the 4C method, 268 data sources, metadata, lineage, data quality and security.
Accelerators
ITSM Intelligence, SLA Integrity & Audit Intelligence, the Access Provisioning Squad, MCP Studio and RFP agents — plus cross-industry accelerators for reconciliation, close and FP&A.
Supervised digital workforce
AI agents that do the routine work end to end, with people approving exceptions and every step logged.
Nine service lines cover the lifecycle, organised the way capability centres buy them.
Select a stage on the wheel, or start from your role.
Set direction and the rules the data must meet.
Data & AI StrategyMaturity, GCC value scorecard, AI operating model Data Governance, Privacy & IT ControlsContinuous ITGC testing, DPDP Act, AI governanceEngineer the platforms and the AI that runs on them.
Data Engineering & Platform ModernisationITSM, CMDB, logs, ERP & HRMS on one platform AI & Agentic EngineeringAgent squads, MCP tools, LLM security guardrailsChange how a capability-centre function works, end to end.
IT Service & OperationsITSM intelligence, SLA risk, AIOps Security, Access & IT ControlsAgentic provisioning, leavers, control monitoring Finance, HR & Business ServicesR2R, P2P, O2C, onboarding & HR helpdesk GCC Capability & AI CoEUse-case pipeline, RFP agents, programme agentsKeep it healthy and improving after go-live.
Managed ServicesDataOps, MLOps & AgentOps under SLAsEvery service line runs on the same framework, accelerators and digital workforce. All services in detail →
A repeatable 4C method turns raw operations data into production-ready capabilities in 30–45 days.
- Step 1 · Week 1–2Connect268 data sources: ITSM, monitoring and logs, CMDB, identity and HRMS, ERP ledgers, and runbooks and documents; batch and streaming.
- Step 2 · Week 2–4CurateStandardise, cleanse and enrich: MDM, de-duplication and data quality rules.
- Step 3 · Week 3–5ContextualizeMetadata catalog, critical data elements, multi-hop lineage, ownership and policy.
- Step 4 · Week 4–6ConsumeData products, APIs, BI, NLQ GenAI studio and the agents behind each accelerator.
Twelve capability layers give every capability centre one blueprint — and one way to measure progress.
The same layers we build and score in the maturity assessment. Hover a layer to see what it does.
Runs in your cloud tenancy or on-prem. Code, ontology and agents are handed over — you own what we build. Explore the blueprint →
MIT CISR found enterprises at stages 3–4 perform well above their industry average financially, while those at stages 1–2 perform below it. Source
GCC and cross-industry accelerators mean no service line starts from a blank page.
Pre-built, configurable starting points from delivered work, on the framework. Hover or tap a tile.
GCC & IT services accelerators CIO · CISO · GCC head
Finance shared services CFO
From the service desk to IT controls, our builds turn queues into supervised, evidenced work.
Counts from platforms as built; capabilities, not client results.
Access Provisioning Squad. Reading a request and granting it are separate steps, with a named approver in between and every step logged. Illustrative request shape.
30 agent roles across 8 capability-centre domains — your people supervise the exceptions.
Agents work end to end across the service desk, cloud operations, engineering, security, finance, HR, the AI CoE and audit. Policy decides what goes straight through; people approve the exceptions — and no access grant, production change or posting is made by an agent alone.
SLA Risk Sentinel · L1
Recurring Problem Analyst · L1
ITSM Copilot · L1
Probable-Cause Analyst · L1
Runbook Assistant · L2
FinOps Analyst · L1
Defect Triage Agent · L3
API Tool Builder · L1
Release Risk Scorer · L1
Provisioning Bot · L2
Leaver Reconciler · L1
Security Alert Triage · L1
Invoice Match Agent · L2
Variance Commentary Writer · L1
Regulatory Report Preparer · L2
HR Helpdesk Assistant · L3
Attrition Signal Analyst · L0
Model & Agent Evaluator · L0
Bid Response Writer · L1
Programme Health Agent · L1
ITGC Control Tester · L0
Evidence Pack Builder · L1
Click a bar to see the agents in it.
Agent designs from our AI & Agentic Engineering practice — not client results. The live control-room demo runs on banking sample data.
An agent does the legwork on every case; a person makes the call when policy says so.
Illustrative replay: a new joiner needs access to a finance application and a shared folder. The access-provisioning squad works it.
- 1Source systemsAn access request arrives in the ITSM queue for a new joiner: a finance-application role and a shared-folder permission.
- 2Data fabricThe ticket, the HRMS joiner record and the role catalogue land within minutes, quality-checked, as governed data products.
- 3Metadata & ontologyRequest, employee, manager and approver resolve to the same identity record, with lineage back to each source.
- 4ContextRole catalogue, segregation-of-duties rules, the approval matrix and similar past grants are assembled — only what this squad may see.
- 5AgentThe queue monitor reads the request with an LLM and calls tools through the secure AI gateway:
get_ticketget_hr_recordcheck_sod_rules; it proposes the role set with evidence and a confidence score. - 6PolicyKill switch, autonomy level, confidence ≥ 0.90 and the segregation-of-duties check run — and any access grant always goes to a person.
- 7Human gateThe named approver reviews the request, evidence and SoD result on one screen and approves, edits or rejects.
- 8ActionThe RPA provisioning bot creates the user and assigns the approved permissions — typed, limited and reversible — and the notification agent tells the requester.
- 9AuditEvery task, tool call and decision lands in the execution log; outcomes feed the evaluation that decides whether autonomy can change.
Autonomy is set per agent and raised only on evidence — inside hard guardrails.
Confidence threshold
Straight-through only if confidence, amount limit and evidence checks pass.
Change limits
Access grants, production changes and ledger postings always go to a person, whatever the confidence.
Named human owner
Approves, edits or rejects every exception; decisions about people and audit findings always need a person.
Everything logged
Every plan, tool call and decision; overrides feed evaluation. Personal data masked in prompts, in line with the DPDP Act.
Kill switch
One control halts every agent at once.
What could a supervised agent squad free up in your business?
Move the sliders to your own volumes. Agents work the cases; people review only the exceptions.
Estimate only, not a quote or a SCIKIQ result. Manual hours = cases × minutes ÷ 60. Supervised hours = cases × (1 − STP share) × review minutes ÷ 60. Hours saved = manual − supervised. FTE = hours saved ÷ 150. Cost saved = hours saved × cost per hour, shown in rupees (1 lakh = ₹1,00,000; 1 crore = ₹1,00,00,000). Supervisor capacity = 150 h × 60 ÷ ((1 − STP share) × review minutes). Excludes platform and run costs.
Faster than services alone, a better fit than software alone.
How the SCIKIQ model compares with the usual ways capability centres deliver data, AI and agentic automation.
| Criterion | Big-4 / SIservices only | Point productssoftware only | In-house build | SCIKIQplatform + accelerators + delivery |
|---|---|---|---|---|
| Time to production value | 6–12 months | 3–6 months + integration | 12–18 months | 30–45 days |
| ITSM, controls & shared-services depth | Generic methods | One use case | Build | Pre-built |
| Reusable data foundation | Rebuilt per project | Vendor-specific | Build | SCIKIQ Data Fabric |
| Ready-made accelerators | Varies | Single product | None | GCC + cross-industry |
| Supervised AI agents in operations | Pilots / PoCs | Copilot features | Build & govern | Agent squads with guardrails & AgentOps |
| Process change & adoption | Yes | Left to the centre | Partial | Yes |
| Run & continuous improvement | Separate contract | Product support | Internal team | Managed services |
| Total cost of ownership | High | Medium–High | Very High | Low |
Big-4 / SI (6–12 months), point products (3–6 months + integration) and in-house builds (12–18 months) are compared on larger screens.
Four phases, each ending with something you keep — and three ways to buy it.
Discovery & Planning
You receive a maturity assessment and target blueprint, with a prioritised roadmap.
Gate: pilot scope signed offAnalysis & Design
Target-state design and ontology, mapped to your controls; agent autonomy agreed with security, operations and finance.
Gate: design authorityBuild & Deploy
Landing zone as code, pipelines, accelerators and agents configured and tested on real data.
Gate: go-live readinessSupport & Embed
Runbooks, evaluation suites and knowledge transfer to a trained team.
Gate: handover sign-offStaff Augmentation
Architects, data and AI engineers embedded in your teams, under your delivery lead.
Project Delivery
Outcome-based delivery of an accelerator or platform build by a SCIKIQ pod, with phase gates.
Managed Services
We run and improve the platform, models and agents — DataOps, MLOps and AgentOps under SLAs.
A 30–45 day accelerator pilot proves value on one use case before you commit to scale.
Fixed scope, fixed timeline, agreed success criteria — and a scale-up business case at the end.
One accelerator & its agent squad
Typically ITSM intelligence for one service queue, access provisioning for one application, or reconciliation for one entity, with agents starting at “act with approval”.
2–3 source systems
e.g. the ITSM tool, the HRMS and the directory — or ERP ledgers, bank statements and intercompany balances.
One business unit
e.g. one service tower, one business unit served or one entity, with a named owner and SMEs for rules and UAT.
SCIKIQ pod
Engagement lead, data engineer, ITSM or finance SME, AI / agent engineer — India-based, alongside your teams.
Success-criteria targets are agreed in week 1.
Three steps take us from this conversation to value in production.
Start small with one accelerator, prove it, then scale on the same framework.
Scoping workshop
Walk through your data landscape and pain points, pick the pilot use case and agree success criteria.
1–2 weeksAccelerator pilot
Deploy one accelerator and its agent squad on the framework against live data, and measure the result.
30–45 daysScale & embed
Roll out across business units and further accelerators through project delivery or managed services.
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