Services

Nine services, mapped to the capability-centre value chain.

Capability centres don't buy "data and AI" — they buy faster resolution, SLAs they can defend, access that is right on day one and gone on the last, a cleaner close, and proof for the parent of what the centre is worth. Below, each SCIKIQ service line is mapped to the domains where it does that work — for global capability centres, IT and BPM service providers and enterprise shared services in India — with the use cases, the KPIs it moves and the accelerators behind it.

See the value-chain map How we add value, by domain Find your service by role
Chapter 2 · Services on the value chain

Where each service line works on the capability-centre value chain

Read across a row to see where a service line leads and where it supports. Read down a column to see the team a capability centre gets in that domain. Select any domain to see use cases, the KPIs we help move and the working accelerators.

9
Service lines
8
Value-chain domains
15
Lead roles across the map
29
Supporting roles across the map
Leads the work Supports Hover a dot for detail · select a domain to explore it
01 · Advise
Data & AI StrategyStrategy
Data Governance, Privacy & IT ControlsGovernance
02 · Build
Data Engineering & Platform ModernisationData platform
AI & Agentic EngineeringAI & agents
03 · Transform
IT Service & OperationsIT ops
Security, Access & IT ControlsSecurity & controls
Finance, HR & Business ServicesBusiness services
GCC Capability & AI CoEAI CoE
04 · Run
Managed Services: DataOps, MLOps & AgentOpsManaged
Service lines engaged 6 6 6 5 5 6 5 5

The mapping shows where each service line typically leads or supports; every engagement is scoped to the business. The value chain itself is explained on the overview.

How we add value

Domain by domain: from data to a measurable outcome

Each domain follows the same path — source data, a governed data product, AI and agents, an outcome the business measures. KPIs are the measures we help you move and track; we agree targets with you, we don't promise them in advance.

Domain 1 of 8

IT service management & service desk

Ticket volumes grow faster than the desk, the same incidents return under new numbers, and SLA breaches are seen after the fact.

Data
Tickets, comments, SLAs & knowledge
Data product
Service & ticket data product
AI & agents
Copilot, triage & SLA-risk agents
Outcome
Faster resolution, fewer breaches

GCC & IT services use cases

  • Ticket classification, routing and duplicate detection
  • SLA-breach risk scoring before the clock runs out
  • Recurring-issue clusters turned into problem records
  • Copilot answers with the evidence attached

KPIs we help you move

Mean time to resolveSLA attainmentFirst-contact resolutionRepeat-incident rate

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working ITSM Intelligence Platform Run an agent live
Domain 2 of 8

Infrastructure, cloud & AIOps

Thousands of alerts, mostly noise; cloud spend reported a month late; events correlated by hand during an outage.

Data
Alerts, logs, CMDB & cloud bills
Data product
Observability data product
AI & agents
Correlation, forecast & FinOps agents
Outcome
Less noise, faster recovery

GCC & IT services use cases

  • Event correlation and alert-noise reduction
  • Probable cause from change history and service maps
  • Incident forecasting for staffing
  • Cloud cost and idle-resource analytics

KPIs we help you move

Alert-to-incident ratioMean time to detectChange failure rateCloud cost per service

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Control Tower ITSM Intelligence Platform
Domain 3 of 8

Application & product engineering

India teams now own products, not tickets; release quality and productivity are judged globally, and AI coding tools outrun their guardrails.

Data
Repos, pipelines, tests & backlogs
Data product
Delivery data product
AI & agents
Test, triage & tool-generation agents
Outcome
Faster, safer releases

GCC & IT services use cases

  • Engineering productivity and flow metrics
  • Test generation and defect triage with GenAI
  • APIs turned into governed agent tools
  • Release-risk scoring from change and defect history

KPIs we help you move

Lead time for changesEscaped defectsDeployment frequencyTest coverage

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working MCP Studio LLM Security Guardrails
Domain 4 of 8

Cyber security & access governance

Joiners and leavers run on tickets, access lingers after exit, and incidents must be reported to CERT-In, the parent and clients with evidence.

Data
HRMS, directory, access & security logs
Data product
Identity & access data product
AI & agents
Provisioning, review & triage agents
Outcome
Right access, provable controls

GCC & IT services use cases

  • Access provisioning behind named approvals
  • Joiner / leaver reconciliation
  • Access-review campaigns prepared for reviewers
  • Security-alert triage and incident timelines

KPIs we help you move

Time to provision accessOrphaned accountsAccess-review completionIncident reporting time

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Access Provisioning Squad Agent governance
Domain 5 of 8

Finance & accounting shared services

R2R, P2P and O2C for many entities and ERPs at once; reconciliations, vendor queries and commentary consume the team at month-end.

Data
ERP ledgers, banks, invoices & GST
Data product
Multi-entity finance data product
AI & agents
Matching, invoice & commentary agents
Outcome
A faster, cleaner close

GCC & IT services use cases

  • Auto-matching and break investigation across entities
  • Invoice capture and three-way match
  • Variance commentary drafted by AI
  • Vendor and customer query handling

KPIs we help you move

Days to closeAuto-match rateInvoice cycle timeCost per transaction

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working CLARION Enterprise Reconciliation NARRATOR
Domain 6 of 8

HR & people operations

Every centre competes for the same engineers; onboarding and access set-up are slow and attrition signals arrive at the exit interview.

Data
HRMS, recruitment, skills & surveys
Data product
People data product (DPDP-compliant)
AI & agents
Onboarding, helpdesk & skills agents
Outcome
Productive from day one

GCC & IT services use cases

  • Onboarding orchestration: accounts, assets, access
  • Attrition-risk signals for managers
  • Skills inventory and internal mobility
  • Employee helpdesk answered from policy

KPIs we help you move

Time to productivityVoluntary attritionInternal fill rateHR ticket resolution time

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Access Provisioning Squad RPA Automation
Domain 7 of 8

Data, analytics & AI CoE

Parents ask the centre to lead AI, but use cases, data access, model risk and value tracking differ by business unit.

Data
Platforms, use cases, models & KPIs
Data product
AI portfolio data product
AI & agents
Pipeline, evaluation & value agents
Outcome
AI that pays back, governed

GCC & IT services use cases

  • Governed use-case pipeline from idea to value
  • Model and agent inventory with evaluations
  • Self-service analytics and natural-language query
  • GCC value scorecard for the parent

KPIs we help you move

Use cases in productionValue realised per use caseTime from idea to pilotModels with documented evaluation

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Maturity assessment RFP & Bid Response Agents
Domain 8 of 8

Governance, risk & audit

Client contracts, SOX ITGC, ISO 27001 and the DPDP Act ask for proof; SLAs can be gamed and findings are found by auditors months later.

Data
ITSM, change, access & control libraries
Data product
Control evidence data product
AI & agents
SLA-integrity & ITGC test agents
Outcome
Controls tested on all the data

GCC & IT services use cases

  • SLA-integrity analysis of pauses and closures
  • Continuous ITGC testing: CAB bypass, leaver access
  • Explainable findings with reasoning chains
  • Control diary of tests and remediation

KPIs we help you move

Control exceptions foundFindings closed on timeRepeat audit findingsEvidence preparation time

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working SLA Integrity & Audit Intelligence SOX Compliance Data Governance Hub
Find your service

Start from your role

Each service line has a clear owner on the client side. Pick yours to jump to the services most teams like yours start with.

GCC head
Managing director / head of the capability centre
CIO / CTO
Chief Information & Technology Officer
ITSM / Ops
Head of service delivery, ITSM & infrastructure
CISO
Chief Information Security Officer & audit
Finance SSC
Head of finance shared services
HR
Head of HR & people operations
01

Advise

Set direction, make the business case and put the rules in place that data and AI must meet.

Advise · Service line

Data & AI Strategy

ForGCC headCIO / CTOParent CIO
On the value chainITSM & service deskCloud & AIOpsApp engineeringF&A servicesHR & people opsAnalytics & AI CoERisk & audit

The client problem

The parent wants its India centre to lead on AI, but pilots multiply across towers and business units with no shared view of maturity, value or ownership — and no answer to "what is the centre worth?".

Outcomes

  • A maturity baseline across 12 capability layers and five stages, scored on evidence rather than opinion
  • A prioritised AI roadmap across service desk, operations, engineering, finance and HR, with a business case for each
  • A GCC value scorecard the parent and the centre both trust

What we do

  • Data & AI maturity assessment
  • AI strategy & use-case prioritisation
  • GCC value scorecard & capability roadmap
  • AI operating model & centre of excellence design

Typical engagements

  • AssessmentMaturity assessment and roadmap
  • Pilot · 30-45 daysProve the top-ranked use case on your data
  • BuildRoadmap delivered through our Build and Transform services
  • Managed runValue tracking and roadmap refresh

Delivered with

Maturity assessment Value calculator SCIKIQ Data Fabric
Advise · Service line

Data Governance, Privacy & IT Controls

ForCISOHead of audit / GRCDPO
On the value chainSecurity & accessF&A servicesHR & people opsAnalytics & AI CoERisk & audit

The client problem

Client contracts, SOX IT general controls, ISO 27001 and the DPDP Act all ask for evidence of who accessed what and why. Controls are tested on samples, findings arrive months late, and AI adds a new layer to govern.

Outcomes

  • Controls tested continuously on all the data, with explainable findings
  • Personal data mapped, minimised and handled with purpose and consent under the DPDP Act
  • AI and agent governance: inventory, policy, approvals, evaluation and a kill switch

What we do

  • Continuous ITGC testing: change, access, operations
  • SLA-integrity and contractual-control analytics
  • DPDP Act data mapping, consent and retention
  • Metadata, lineage & data quality
  • AI & agent governance, LLM security guardrails

Typical engagements

  • AssessmentControls and privacy gap review
  • Pilot · 30-45 daysContinuous testing of one control family
  • BuildControl library, monitoring and evidence store
  • Managed runOngoing control monitoring and stewardship

Delivered with

SLA Integrity & Audit Intelligence SOX Compliance Data Governance Hub Agent governance
02

Build

Engineer the data platforms and the AI that runs on them, with governance built in.

Build · Service line

Data Engineering & Platform Modernisation

ForCIO / CTOHead of data
On the value chainITSM & service deskCloud & AIOpsApp engineeringF&A servicesAnalytics & AI CoE

The client problem

Tickets, alerts, CMDB, ledgers and HR data sit in tools inherited from every business unit. Every report, model or agent starts with another integration, and nothing agrees on who owns what.

Outcomes

  • One governed, cloud-agnostic data platform on Azure, AWS or hybrid
  • A service, asset and ownership model across ITSM, CMDB and HRMS
  • Batch and streaming pipelines for logs, events and tickets, with lineage

What we do

  • ITSM, CMDB, observability, ERP and HRMS integration
  • Service & ownership data model
  • Log and event pipelines for AIOps
  • Lakehouse / warehouse on Azure or AWS
  • Infrastructure as Code & secrets management

Typical engagements

  • AssessmentData estate and target-architecture review
  • Pilot · 30-45 daysConnect and curate priority sources end to end
  • BuildPlatform build in sprints, tested with real data
  • Managed runDataOps under agreed SLAs

Delivered with

SCIKIQ Data Fabric 268 data sources ITSM Intelligence Platform
Build · Service line

AI & Agentic Engineering

ForCIO / CTOHead of AI CoECISO
On the value chainITSM & service deskCloud & AIOpsApp engineeringSecurity & accessF&A servicesHR & people opsAnalytics & AI CoE

The client problem

Agent pilots stall at the security review: agents with access to production, no autonomy limits, no approval step, prompts open to injection and no log of what was done.

Outcomes

  • Supervised agent squads that triage, provision and reconcile, with autonomy set per agent
  • Existing APIs turned into governed tools that agents can call
  • GenAI applications protected by layered security guardrails

What we do

  • Agent design: roles, squads & autonomy levels
  • MCP tool generation from REST and OpenAPI APIs
  • ITSM copilots and knowledge assistants
  • LLM security: injection defence, output filtering, monitoring
  • Evaluation & guardrails: policy, limits, kill switch

Typical engagements

  • AssessmentAgent opportunity and controls review
  • Pilot · 30-45 daysOne agent squad working real cases under your controls
  • BuildSquads integrated with your systems and scaled
  • Managed runAgentOps: monitoring, overrides, drift

Delivered with

Access Provisioning Squad MCP Studio LLM Security Guardrails Run an agent live
03

Transform

Domain practices that change how a capability centre works, end to end, with our accelerators as the starting point.

Transform · Service line

IT Service & Operations (ITSM + AIOps)

ForHead of ITSM / service deliveryHead of infrastructureCIO
On the value chainITSM & service deskCloud & AIOpsApp engineeringSecurity & accessRisk & audit
6
ML models in the ITSM platform: anomalies, forecasts, resolution time, SLA risk, recurring issues, bottlenecks
8
Intent types the ITSM copilot understands
5
Steps in the AI-narrated SLA-integrity investigation
3
Cooperating agents in the access-provisioning squad

Counts from the platforms we have built; capabilities, not client results.

The client problem

Ticket and alert volumes outgrow the teams. The same incidents recur, SLA breaches are seen too late, and engineers spend their time on triage and lookups instead of the fix.

Outcomes

  • Tickets classified, routed and de-duplicated, with SLA risk visible before the breach
  • Recurring issues turned into problem records and permanent fixes
  • Alert noise reduced and probable causes suggested from changes and service maps

What we do

  • ITSM copilot, classification & routing
  • SLA-risk prediction & recurring-problem management
  • Event correlation & AIOps
  • Knowledge base & runbook automation
  • Incident forecasting & staffing

Typical engagements

  • AssessmentService-desk and operations diagnostic
  • Pilot · 30-45 daysOne queue or service tower end to end
  • BuildITSM intelligence and AIOps rollout
  • Managed runModel and agent operations under SLA

Delivered with

ITSM Intelligence Platform Control Tower RPA Automation
Transform · Service line

Security, Access & IT Controls

ForCISOIAM leadHead of audit
On the value chainITSM & service deskCloud & AIOpsSecurity & accessHR & people opsRisk & audit

The client problem

Joiners, movers and leavers run on tickets and spreadsheets, access lingers after people leave, and security incidents must be reported quickly to CERT-In, the parent and clients — with evidence.

Outcomes

  • Access provisioned and revoked through agents, behind named approvals
  • Joiner and leaver records reconciled between HR, directory and applications
  • Security incidents and control findings with a complete evidence trail

What we do

  • Agentic access provisioning with approvals
  • Joiner / mover / leaver reconciliation
  • Access-review campaigns
  • Security-alert triage & incident timelines
  • Change-management (CAB) control monitoring

Typical engagements

  • AssessmentAccess and controls diagnostic
  • Pilot · 30-45 daysOne application or access process end to end
  • BuildProvisioning squads and control monitoring
  • Managed runAgent operations and control evidence under SLA

Delivered with

Access Provisioning Squad SLA Integrity & Audit Intelligence Agent governance
Transform · Service line

Finance, HR & Business Services

ForFinance shared-services headHR headGBS leader
On the value chainF&A servicesHR & people opsRisk & audit

The client problem

Record-to-report, procure-to-pay, order-to-cash and HR operations run for many entities and systems at once. Reconciliations, vendor queries, onboarding and month-end commentary consume the teams.

Outcomes

  • A faster, cleaner multi-entity close with automated matching
  • Invoices and vendor queries handled by agents, exceptions by people
  • Joiners productive on day one, with accounts and access ready

What we do

  • Reconciliation & close automation (R2R)
  • Invoice capture & three-way match (P2P)
  • Cash application & disputes (O2C)
  • Onboarding orchestration & HR helpdesk
  • Month-end commentary & FP&A

Typical engagements

  • AssessmentShared-services process and data diagnostic
  • Pilot · 30-45 daysOne process or entity end to end
  • BuildRecon, close and HR operations rollout
  • Managed runRecon and close squads under SLA

Delivered with

CLARION Enterprise Reconciliation NARRATOR COMPASS
Transform · Service line

GCC Capability & AI CoE

ForGCC head / MDHead of AI CoEEngineering head
On the value chainApp engineeringHR & people opsAnalytics & AI CoE

The client problem

The centre is asked to move from support to ownership — products, platforms and AI for the parent — but use cases, tools and value tracking differ by team, and bids and proposals still start from scratch.

Outcomes

  • A governed AI use-case pipeline from idea to measured value
  • Engineering productivity and release quality visible across teams
  • Reusable assets — agents, tools, guardrails — shared across business units

What we do

  • AI CoE set-up & use-case pipeline
  • Engineering-productivity & flow analytics
  • Agentic programme & portfolio management
  • RFP & bid response automation
  • Data literacy & DAMA CDMP skills programmes

Typical engagements

  • AssessmentCoE and engineering maturity review
  • Pilot · 30-45 daysOne reusable AI asset for two business units
  • BuildCoE platform, pipeline and shared assets
  • Managed runCoE operations and value tracking

Delivered with

RFP & Bid Response Agents Helios programme management MCP Studio Maturity assessment
04

Run

Keep platforms, models and agents healthy and improving after go-live.

Run · Service line

Managed Services: DataOps, MLOps & AgentOps

ForCIO / CTOHead of service delivery
On the value chainITSM & service deskCloud & AIOpsApp engineeringSecurity & access

The client problem

After go-live, tools change, APIs version, models drift and agents need someone watching approvals, overrides and execution logs — around the clock, across time zones.

Outcomes

  • Platforms, pipelines, models and agents run under agreed SLAs, follow-the-sun
  • Continuous improvement driven by override and evaluation data
  • Your engineers freed from L2/L3 support of the AI estate

What we do

  • Run & L2/L3 support
  • DataOps for ITSM, logs and ERP feeds
  • MLOps
  • AgentOps: logs, approvals, drift, kill switch
  • Continuous improvement

Typical engagements

  • AssessmentRun-readiness and support model review
  • Pilot · 30-45 daysHypercare for a newly live capability
  • BuildMonitoring, runbooks and SLAs
  • Managed runOngoing service under agreed SLAs

Delivered with

Governance & controls SCIKIQ Data Fabric
Our assets

What makes our services faster

Every engagement starts from SCIKIQ IP rather than a blank page. These assets are how we deliver — they come with the service.

1
SCIKIQ Data Fabric

The governed foundation every engagement runs on: the 4C method (Connect, Curate, Contextualize, Consume), 268 data sources, and governance, lineage, data quality, PII detection and masking, an AI/agent layer and security built in — cloud-agnostic on Azure, AWS or hybrid.

Explore the framework
2
Accelerators

GCC and IT services accelerators from platforms we have built — the ITSM Intelligence Platform, SLA Integrity & Audit Intelligence, the Access Provisioning Squad, MCP Studio, RFP agents and Helios — plus cross-industry accelerators such as CLARION, COMPASS and NARRATOR, tailored to your rules, data and controls.

Accelerators by service line
3
Supervised digital workforce

AI agents that plan, call tools and gather evidence on the governed data. Policy decides what goes straight through; people approve everything else; every step is logged and a kill switch halts all agents.

How it works Watch one work
How we engage

From a business outcome to measured value

Every engagement starts from the business outcome, not the technology. We frame it through the same business lens each time, then deliver in five phase-gated stages. Most capability centres start with a discovery and value case for one value-chain domain, then scale to the next on the same foundation.

How we frame an engagement

  1. 1Business outcome & KPI
  2. 2Value-chain domain
  3. 3Decisions
  4. 4Data
  5. 5AI & agents
  6. 6Governance & adoption
  7. 7Measured value

Delivery phases

Phase 1
Discover & value case

Outcome, domain and KPIs agreed; data and process assessment; baseline and business case.

Phase 2
Design

Decisions, data products, models, agents and controls designed for the chosen domain.

Phase 3
Build & integrate

Sprint delivery on the SCIKIQ Data Fabric, integrated with your ITSM, monitoring, CMDB, identity, ERP and HRMS systems; tested on real data.

Phase 4
Deploy & adopt

Go-live, people and process change, agent autonomy limits agreed with operations, quality, procurement and finance; value tracked against the baseline.

Phase 5
Run & scale

Managed service — DataOps, MLOps and AgentOps under SLA — and the next domain on the same foundation.

Each phase ends with a gate signed off by your steering group; a pilot in one domain typically reaches a production-ready capability in 30-45 days. The SCIKIQ Data Fabric we build on

Engagement models

Staff Augmentation

Data engineers, architects, analysts and AI specialists embedded in your teams, under your delivery lead.

Project Delivery

Outcome-based delivery of an accelerator or platform build, owned end-to-end by SCIKIQ with phase gates.

Managed Services

We run and improve your data platforms, models and agents — DataOps, MLOps, AgentOps and support under agreed SLAs.

Weekly status Bi-weekly steering Phase-gated sign-off 30-45 day pilot → scale

Start with the outcome you need

Pick a domain and a KPI. We'll propose a discovery and value case or a 30-45 day pilot and show you what the first weeks look like.

Talk to us Take the self-assessment
Next chapter · 3 of 6
The foundation

Every domain above runs on the same governed data fabric — ITSM, CMDB, logs, identity, ERP and HRMS connected, curated, contextualised and consumed, with lineage from source to regulatory report.

Next chapter: The foundation