Question 1

What does Thinklar do?

Accepted Answer

Thinklar is an Enterprise Technology Company that designs, builds, and deploys AI systems, managed IT services, custom software, mobile apps, cloud infrastructure, and digital marketing solutions for businesses globally. We specialize in Agentic AI, EAOS, HMAS, and end-to-end enterprise technology transformation.

Question 2

What industries do you serve?

Accepted Answer

We work across Financial Services, Healthcare, Retail, Manufacturing, Logistics, Technology, Government, Education, and Professional Services. Our frameworks are industry-agnostic, but we bring deep domain expertise in each vertical through our specialist consulting teams.

Question 3

Where is Thinklar based and do you work globally?

Accepted Answer

Thinklar is headquartered in Noida, India with team members across major Indian cities. We work with enterprises globally — our delivery model is remote-first and we've successfully delivered projects across North America, Europe, Southeast Asia, and the Middle East.

Question 4

How do I start working with Thinklar?

Accepted Answer

The fastest way is to book a free Strategy Session through our contact page. In 60 minutes, we'll understand your goals, assess your current state, and outline an initial roadmap. There's no obligation — it's a working session, not a sales call.

Question 5

What is EAOS and how does it differ from traditional AI platforms?

Accepted Answer

EAOS (Enterprise Autonomous Operating System) is a comprehensive intelligence layer spanning your entire enterprise. Unlike point AI tools that solve isolated problems, EAOS connects all your agents, data sources, workflows, and business systems into a unified autonomous environment that can reason, plan, and execute across departments simultaneously.

Question 6

What is HMAS and why do enterprises need it?

Accepted Answer

HMAS (Hierarchical Multi-Agent Autonomous Systems) is our proprietary architecture for coordinating hundreds of specialized AI agents. It organizes agents in a Chief → Domain → Task → Infrastructure hierarchy, ensuring clear accountability, specialized expertise, and enterprise-grade scalability. You need HMAS when single-agent solutions can't handle cross-functional complexity.

Question 7

What AI models and LLMs do you work with?

Accepted Answer

We are fully model-agnostic. Through our AI Gateway, we support all major LLMs: OpenAI GPT-4o, Anthropic Claude, Google Gemini, Meta Llama, Mistral, Qwen, Cohere, and custom fine-tuned models. Our Gateway enables intelligent routing, cost optimization, and automatic fallback across providers.

Question 8

What is an AI Gateway and do I need one?

Accepted Answer

An AI Gateway is a centralized control plane for all your LLM traffic. It handles model routing, rate limiting, cost tracking, prompt injection defense, audit logging, and compliance controls. If you're using more than one AI model in production — or plan to — you need a Gateway.

Question 9

How long does a typical project take?

Accepted Answer

A focused MVP — whether an AI agent system, a custom software product, or a managed services onboarding — typically takes 4–8 weeks. Larger enterprise-wide implementations range from 3–12 months depending on scope, integrations, and team size. We'll give you a precise timeline after the initial assessment.

Question 10

What systems can you integrate with?

Accepted Answer

We integrate with the full enterprise stack: Salesforce, SAP, Oracle, Microsoft Dynamics, ServiceNow, Workday, Jira, Confluence, Slack, custom databases, data warehouses (Snowflake, BigQuery, Databricks), legacy ERP systems, and any REST/GraphQL/gRPC API.

Question 11

Do you build with existing platforms or from scratch?

Accepted Answer

Both. We can extend your existing technology investments (Azure OpenAI, AWS Bedrock, GCP, existing ERPs) with our solutions, or design a full custom architecture from the ground up. We'll recommend the right approach based on your current infrastructure, budget, and timeline.

Question 12

What does your typical engagement look like?

Accepted Answer

Most engagements follow four phases: (1) Discovery & Assessment — understanding your current state and defining the solution; (2) MVP Build — delivering the first production release; (3) Scale & Integrate — expanding across departments or channels; (4) Ongoing Support — monitoring, optimization, and iteration.

Question 13

How do you handle enterprise security and data privacy?

Accepted Answer

Security is designed into every layer. We implement encryption at rest and in transit, RBAC, audit logging, data residency enforcement, and PII masking. For AI systems we add prompt injection defense and agent action controls. We operate within your existing security perimeter — your data never leaves your control boundary without explicit authorization.

Question 14

What compliance frameworks do you support?

Accepted Answer

We support SOC 2 Type II, ISO 27001, GDPR, HIPAA, CCPA, and industry-specific frameworks including RBI, SEBI, IRDAI (India) and FINRA/SOX (US). Our governance documentation covers audit trails, explainability logs, and the records needed for regulatory audits.

Question 15

Can your AI agents take actions without human approval?

Accepted Answer

Yes, but only within policy boundaries you define. Our architecture supports both fully autonomous and human-in-the-loop modes at the task level. You can configure which actions require approval, set spending limits, restrict data access, and define kill switches — full control without sacrificing automation value.

Question 16

How is Thinklar's work priced?

Accepted Answer

We offer three engagement models: (1) Fixed-scope projects — for well-defined deliverables with a clear timeline; (2) Retainer model — for ongoing development, managed services, and iteration; (3) Outcome-based — for select engagements where we price against measurable business outcomes. We'll recommend the right model during your Strategy Session.

Question 17

Do you have a minimum engagement size?

Accepted Answer

Our minimum project engagement is ₹15L / $18K USD. This ensures we can deliver enterprise-grade architecture and quality. Retainer engagements start from ₹4L / $5K USD per month. For startups or early-stage organizations, we offer structured assessment packages starting at ₹2.5L.

Question 18

Do you offer support after the initial build?

Accepted Answer

Yes. We offer ongoing retainers covering system monitoring, performance optimization, bug fixes, feature iterations, and quarterly reviews. Most clients retain us post-launch because complex systems need continuous attention to stay performant and cost-efficient.

Question 19

Can we start small and scale up?

Accepted Answer

Absolutely — this is our recommended approach. We design all systems with a modular architecture so you can start with a single high-value use case, prove ROI, and expand systematically. Starting small reduces risk and builds organizational confidence before a larger rollout.

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