Prem Moola

Prem Moola

Senior Technology Executive | AI & Data Transformation Leader | Agentic AI | Cloud & Data Engineering | Ex-Goldman Sachs | BNY Mellon

[Diggt Habit @diggthabit]

For personal operating discipline—like habit tracking—the source of truth must be deterministic and instantly available. The most reliable source of truth is always local.

[Prem Moola @moolaprem]

Ship 3 distinct SaaS products (scheduling, property mgmt, habit tracking) without incurring 3 separate tech stacks, 3 separate maintenance teams, and 3 separate infra bills? answer is the platform layer. It’s not features; it’s shared operating economics.https://t.co/CyFjQYRJH8

[Diggt Shyftly @Diggt_Shyftly]

Core operational pain point for local shops and small chains is rarely sales,it’s scheduling discipline
businesses treat workforce scheduling as an administrative chore, not a critical business workflow. Treating it this way guarantees friction and unpredictable coverage gaps

[SRASTA AI @srasta_ai]

Enterprise AI needs guardrails. It's not just about models; it's about *where* they run & what they can access. #AI #Governance #EnterpriseAI
Learn more: https://t.co/i5Qx7FV20X

[Prem Moola @moolaprem]

Ship 3 distinct SaaS products (scheduling, property mgmt, habit tracking) without incurring 3 separate tech stacks, 3 separate maintenance teams, and 3 separate infra bills? answer is the platform layer. It’s not features; it’s shared operating economics.https://t.co/CyFjQYRJH8

[SRASTA AI @srasta_ai]

The Problem with "Black Box" AI:
When an LLM moves from a sandbox prompt to a production workflow, it doesn't just "generate text." It manages state, memory, and tools. If those boundaries are undefined, the system runs into architectural risk: memory drift, uncontrolled data leakage, and un-auditability.
#SrastaAI #AIControlPlane #AIRuntime #AIInfrastructure #AgenticAI #PrivateAI #EnterpriseAI #LLMOps #AIOps #OpenWeights

[Prem Moola @moolaprem]

The narrative shift in AI leadership isn't about the model's capability—it's about the operational layer of control.
https://t.co/SRgCpGNu8x
#SrastaAI #AIControlPlane #AIRuntime #AIInfrastructure #AgenticAI #PrivateAI #EnterpriseAI #LLMOps #AIOps #OpenWeights

[SRASTA AI @srasta_ai]

The Governance Gap: Moving AI from Demo to Deterministic Workflow https://t.co/eR3I8LfJ3N

#SrastaAI #AIControlPlane #AIRuntime #GovernedAI

[SRASTA AI @srasta_ai]

The model is not the moat.

The operating layer is.

Enterprise AI needs identity, policy, routing, governed memory, evals, audit, observability, deployment, rollback, and cost control around the model.

That is where Srasta is focused.

https://t.co/VJMxoYzEGK

#SrastaAI #AIControlPlane #AIRuntime #AIInfrastructure #AgenticAI #PrivateAI #EnterpriseAI #LLMOps #AIOps #OpenWeights

[SRASTA AI @srasta_ai]

Current enterprise AI deployments often treat the LLM interaction as a stateless call: Prompt $\to$ Model $\to$ Answer.
In regulated environments, this is fundamentally insufficient. AI systems require a defined, auditable 'Memory Boundary.'
#SrastaAI #AIControlPlane #AIRuntime #AIInfrastructure #AgenticAI #PrivateAI #EnterpriseAI #LLMOps #AIOps #OpenWeights

[SRASTA AI @srasta_ai]

API model is fast to prototype, but speed comes with fundamental architectural flaw: cost is tied directly to consumption, not business value. You are paying for a token count, not successful business outcome leading to extreme financial variability and difficulty in forecasting.

[SRASTA AI @srasta_ai]

Most enterprise AI deployments treat token usage as utility, but operational architects know it's variable cost model with catastrophic risk.Modern threat isn't hallucination; it's unpredictable infrastructure bill. When AI scales, unpredictable cost becomes a governance problem

[Prem Moola @moolaprem]

Most enterprise AI conversations start with the model.

In production, the model is surrounded by context: documents, permissions, tool results, workflow state, user history, and prior decisions.

That context layer needs governance too. https://t.co/9u2L3oWq4J @srasta_ai

[SRASTA AI @srasta_ai]

I just wrote my first note on Substack https://t.co/RPCZ7NGDMI

[SRASTA AI @srasta_ai]

Enterprise AI pilots rarely stall because the model is bad. https://t.co/1YdX8y9dgL

[SRASTA AI @srasta_ai]

AI token cost is becoming an architecture problem.Once AI moves from experiments into workflows, teams need controlled inference paths, model-routing policy,governed mem, auditable tools, & recovery workflows.
https://t.co/S70plUqK6s
#EnterpriseAI #AIInfrastructure #AIGovernance

[SRASTA AI @srasta_ai]

The model is not the moat. Operating layer is.
Enterprise AI needs identity, policy, routing, governed memory, evals, audit, observability, deployment, rollback, and cost control around the model
Srasta's focuses on that .#EnterpriseAI #AIGovernance
https://t.co/bYpDiRGTNE

[Prem Moola @moolaprem]

Enterprise AI pilots rarely stall because the model is bad. https://t.co/ECNfVVFS2f

[SRASTA AI @srasta_ai]

Srasta is built around a simple idea:
Enterprise AI needs a control plane, not just a chat interface.
It means private inference, governed memory, identity/RBAC, policy-aware tool execution, audit trails, and operator workflows.
The model matters. The operating layer matters more

[Prem Moola @moolaprem]

Most enterprise AI conversations start with the model.

In production, the model is surrounded by context: documents, permissions, tool results, workflow state, user history, and prior decisions.

That context layer needs governance too. https://t.co/9u2L3oWq4J @srasta_ai