What we do · AI Operations

Agentic AI, Run in Production.

Getting AI live is half the job. Keeping it running, watched, and governed is the other half. Parkar runs the core IT you depend on and the AI on top, on one platform, with no handoff to a separate vendor. You get the outcomes. We run everything underneath.

Trusted by enterprises globally 250+ Engagements 24x7 NOC and SOC L1 to L4 support Governed end-to-end
Why this matters

Running Agents is Not Running Servers.

Traditional managed services keep infrastructure healthy. AI adds a whole layer to watch, measure, and govern that they were never built for. The leaders are re-architecting operations around AI. Here is the gap they are closing.

Reactive, Not Predictive

Traditional support waits for the alert, then triages. Agents degrade quietly, and by the time a ticket is filed the damage is done.

Tickets, Not Outcomes

Success measured in tickets closed and hours billed has nothing to say about whether the AI is still doing its job.

No One Watches the Model

Infrastructure stays green while accuracy drifts. A 5% accuracy drop is a real incident, and most ops teams cannot even see it.

A New Attack Surface

Agents are non-human identities with real access. Perimeter security and human IAM were never designed for them.

Two Worlds, One Seam

Traditional MSPs have no AI muscle. AI-ops point tools have no enterprise grounding. The boundary between them is where programmes break.

You do not replace your IT operations. You extend them.

The shift

Managed Services are Being Re-Architected around AI.

The model is moving from waiting for tickets to preventing them, from billing hours to owning outcomes, and from a fixed vendor scope to a shared backlog. The bar for what operations means is rising.

Reactive managed services
  • Engineers wait for the alert, then triage
  • Measured in tickets closed and hours billed
  • A vendor delivering a contracted scope
  • Infrastructure and app health watched
  • Availability and response-time SLAs
AI-first managed services
  • Agents predict degradation and remediate before users notice
  • Measured in business outcomes per product
  • A co-innovation partnership with a shared backlog
  • Model drift, accuracy, and prediction confidence watched
  • Model-performance SLAs, a 5% accuracy drop is a P2 incident
Where Parkar fits

Two Worlds are Colliding. We Run Both.

Most vendors pick a side. Traditional MSPs have no AI muscle, and AI-ops tools have no enterprise grounding. We keep the core IT stable and govern, secure, and orchestrate the AI workforce, with one team and no seam at the boundary.

The IT operations you run today
  • Service desk and ITSM
    The command layer, delivered L1 to L3 under SLA.
  • Cloud operations
    Your infrastructure run, patched, and kept available across the major clouds.
  • Application ops and SRE
    Your apps kept healthy, with the reliability practices a production estate needs.
  • Data platform operations
    Your data platforms managed, monitored, and reliable.
The AI-first layer you add
  • AI trust, risk, and security
    Govern and secure the AI across risk, runtime, and identity.
  • Agentic AI and platform intelligence
    Run the AI workforce, with orchestration, LLMOps, vector, and GPU operations.
  • Model lifecycle
    Evaluation, versioning, and drift detection so the AI stays accurate over time.
  • AI FinOps
    Token-spend and GPU cost kept visible and under control.

One partner across the boundary where most programmes break. The core stays stable while the AI workforce is governed, secured, and run.

Agentic operations

What Running Your Agents in Production Looks Like.

When agents do real work, someone has to watch how they reason, what they call, what they read, and when to pull a human in. This is the operations layer that traditional support does not cover.

Orchestration and Observability

  • Multi-agent workflow monitoring
  • Agent reasoning tracing
  • Tool-invocation governance
  • Human-in-the-loop management

Reliability of What Agents Read

  • Vector index management
  • RAG latency monitoring
  • Retrieval quality checks
  • Re-indexing pipeline operations

Model Lifecycle

  • Evaluation pipelines
  • Model registry and versioning
  • Drift detection
  • Fine-tuning pipeline operations

Cost and Infrastructure

  • Dynamic GPU scheduling
  • Token-spend FinOps
  • Inference-cost optimisation
  • Environment vending
Governance

We Govern Your Agents End to End.

Not a new black box. One governed discipline, operated end to end as part of the AIONIQ platform. Every decision your AI makes is logged, inspectable, and yours to audit.

The AIONIQ governance plane
Model risk, owned and proven
  • AI model inventory across the enterprise
  • Risk scoring and policy enforcement
  • Shadow-AI detection
  • Compliance reporting for the EU AI Act, NIST, and ISO 42001
The AIONIQ identity plane
Every agent, identified and scoped
  • A managed machine identity for every agent
  • Least-privilege access enforcement
  • Automated secret rotation
  • Agent behavioural monitoring

Nothing proprietary you cannot audit or own. Parkar operates it end to end, and the audit trail is yours.

See how AIONIQ builds and runs it
The roadmap

From IT Operations to AI-First, One Rung at a Time.

Three bundles aligned to your AI maturity. You start where you are and expand as you adopt. Each rung stands on its own and sets up the next.

01 · MSP Core

  • Cloud infrastructure operations
  • Application support, L1 to L3
  • Traditional SOC and FinOps
For stable IT operations

02 · AI Secure

  • Everything in Core
  • AI governance and runtime control
  • AI runtime security and FinOps
For safe GenAI adoption

03 · AI Native

  • Everything in AI Secure
  • Agentic orchestration
  • MLOps, LLMOps, vector and GPU ops
For agentic automation
How it runs

A Structured Path, Fully Managed.

A short AI Operations Readiness assessment scores your managed services against the AI-first model and sets the phased path. No commitment to continue.

3–6 MONTHS

Stabilize

Cloud infra ops, 24x7 service desk, application support, and traditional security in place.

6–12 MONTHS

Secure AI

AI governance, shadow-AI detection, runtime protection, and token-cost tracking.

12–18 MONTHS

Scale AI

Agent orchestration, LLMOps, GPU optimisation, and vector operations.

ONGOING

Optimize

Model refinement, cost optimisation, compliance updates, and capability expansion.

You receive the cadence an ops team runs on, daily incident and token-burn trackers and monthly SLA, drift, and AI-risk reviews. Start with predictable bundles, move to outcome-based as the AI layer matures.

Proof

Already Running in Production.

Financial Services · Agentic Operations

PO Approval Agent in Production

18 months of stalled PoCs taken to a governed agent on SAP and Workday in 8 weeks. Policy-as-code, a hard human approval step, and a full audit trail.

70% of routine approvals handled by AI, compliance reporting time down 30%, regulatory audit passed
Manufacturing · IT Operations

Unified IT and OT Monitoring

Siloed IT and OT, mean time to acknowledge averaging 28 minutes, availability at 97.8%. Unified monitoring with a 24x7 NOC and automated runbooks.

MTTA from 28 minutes to under 4, availability to 99.4%, P1 and P2 incidents down 70%
Healthcare · AI Governance

Governance without a Black Box

A model inventory and shadow-AI detection stood up end to end, with a managed identity for every agent, all operated by Parkar.

Audit-ready AI governance, with the audit trail the enterprise owns
Technology · Agentic Operations

Agents Watched and Costed

Multi-agent workflows monitored with reasoning traces and retrieval-quality checks, and inference spend kept visible with token-level FinOps.

Agents in production, observed, governed, and within budget

Tell Us Where Your Operations are Today.

The AI Readiness Diagnostic scores where you stand and the road to AI-first operations. A few minutes, and you get a report and the right next step.