Question 1

Do you build AI systems from scratch, or only fix existing ones?

Accepted Answer

Both. We build AI-native products such as RAG applications, autonomous AI agents, multi-agent systems, and AI-powered copilots from the ground up. We also audit, secure, evaluate, optimize, and scale existing AI systems in development or production.

Question 2

We already have an AI application. Can you work with our existing codebase?

Accepted Answer

Yes. We first analyze your architecture, identify failure points, and recommend targeted engineering improvements. Where possible, we optimize the existing codebase rather than replacing it.

Question 3

Can you work with our existing engineering team?

Accepted Answer

Yes. We collaborate with developers, AI/ML engineers, DevOps teams, and CTOs. We can own a specific AI engineering stream or work within your workflow through GitHub, documentation, reviews, and scheduled syncs.

Question 4

How do you know whether our AI actually works?

Accepted Answer

We establish an evaluation baseline before major architectural changes. Depending on the system, we measure retrieval precision and recall, answer correctness, hallucination rate, agent task success, latency, token spend, security risks, and regression results.

Question 5

Can you guarantee that our AI will never hallucinate?

Accepted Answer

No, and we will not pretend otherwise. We engineer systems to minimize, detect, and isolate hallucinations through better retrieval, grounding, guardrails, model routing, and fallback mechanisms. Critical workflows can use deterministic paths when confidence is low.

Question 6

Will our data be safe?

Accepted Answer

We design around your security requirements. Depending on the engagement, this can include access controls, data minimization, secret management, encryption, isolated containers, private cloud or on-premise deployment, and restricted model flows.

Question 7

Do you need access to our production environment?

Accepted Answer

Not necessarily. We follow least privilege. Development and evaluation happen in controlled staging environments, with production access requested only when strictly required and under agreed permissions.

Question 8

Can you deploy the AI system into our AWS, Azure, or GCP infrastructure?

Accepted Answer

Yes. We work within your cloud provider to build production-ready infrastructure, including Docker, Kubernetes, CI/CD pipelines, observability, and evaluation harnesses.

Question 9

What happens if the AI works in development but fails after deployment?

Accepted Answer

We investigate the gap between development and production, including data distribution, retrieval drift, concurrency, latency, infrastructure bottlenecks, prompts, and tool permissions. Then we reproduce and fix the production issue against a measurable baseline.

Question 10

Can you reduce our OpenAI, Anthropic, or Gemini API costs?

Accepted Answer

Yes. We analyze token usage, model routing, semantic caching, payload size, and workflow design to reduce cost per request without sacrificing the required response quality.

Question 11

Do you only work with OpenAI models?

Accepted Answer

No. We select models based on cost, privacy, latency, reliability, and task requirements. We can integrate commercial APIs or host open-source models in a private cloud.

Question 12

Can you build an AI agent that performs actions, not just chats?

Accepted Answer

Yes. We build action-oriented agents that work with APIs, databases, external tools, and internal systems. High-risk operations can include human approval steps and strict permission limits.

Question 13

Can you build multi-agent systems?

Accepted Answer

Yes, when multi-agent architecture provides a clear engineering advantage, such as Planner, Specialist, and Reviewer workflows. If one agent or a deterministic script is more reliable and cost-effective, we recommend that instead.

Question 14

Can you integrate AI with our CRM, ERP, or internal APIs?

Accepted Answer

Yes. We build secure integrations with databases, internal REST or gRPC services, CRMs, ERPs, and third-party tools while maintaining strict authentication and scope boundaries.

Question 15

How long does a typical project take?

Accepted Answer

Timelines are defined before kickoff. Focused audits and optimization usually take 1 to 2 weeks. RAG applications, agents, and infrastructure builds typically take 3 to 8 or more weeks depending on scope.

Question 16

How much does a project cost?

Accepted Answer

We use fixed-scope engagements rather than hourly billing. Indicative ranges are: Diagnostic Audits ₹75,000 to ₹1.5 Lakhs in India or $1,500 to $3,500 globally; Fix and Optimization ₹1.5 Lakhs to ₹3 Lakhs or $3,000 to $6,000; End-to-End Builds ₹2.5 Lakhs to ₹10 Lakhs+ or $6,000 to $25,000+.

Question 17

Do we have to pay for the entire project upfront?

Accepted Answer

No. Major builds can use milestone payments: 40% at SOW signing and kickoff, 30% at prototype and evaluation dataset delivery, and 30% at production deployment and handover. Diagnostic audits under ₹1 Lakh or $2,000 are billed upfront.

Question 18

What happens if the scope changes during development?

Accepted Answer

The Statement of Work defines the scope and technical boundaries, including up to two major workflow iteration cycles during prototyping. Additional features are handled through a formal Change Request or separate SOW.

Question 19

Do you provide the source code after the project?

Accepted Answer

Yes. After final payment, we hand over ownership of source code, deployment configuration, evaluation datasets, infrastructure scripts, and operational runbooks as specified in the contract.

Question 20

What happens after you finish the project?

Accepted Answer

You retain ownership with zero vendor lock-in and can hand the system to your internal team. We can also provide ongoing engineering support, feature expansion, and evaluation monitoring through a recurring engagement.

Question 21

What if AI is not actually the right solution for our problem?

Accepted Answer

We will tell you directly. If a standard backend service, database query, or deterministic rule engine is cheaper, faster, and more reliable than an LLM, we will recommend that instead.

Question 22

Do you sign an NDA?

Accepted Answer

Yes. Mutual Non-Disclosure Agreements are executed before reviewing proprietary codebases, system architectures, or sensitive business data.

Question 23

Can you work with early-stage startups?

Accepted Answer

Yes. We partner with early-stage teams to build MVPs, improve early architectures, and turn validated manual workflows into reliable AI-native products.

Question 24

Do you work only with companies in India?

Accepted Answer

No. CuriousDevs is based in Gurugram, India, and supports remote delivery for teams across North America, Europe, and the Middle East through dedicated international rate schedules.

Question 25

What makes CuriousDevs different from a traditional software development agency?

Accepted Answer

We do not offer general web or mobile outsourcing. We are an AI Engineering Studio focused on the reliability layer around AI systems: Architect, Build, Evaluate, Secure, Deploy, and Scale. We focus on measurable performance in real production environments.

Clear answers before we start working.