Question 1

What AI tasks can your engineers help us with?

Accepted Answer

We build end-to-end AI applications: prompt engineering, RAG databases, agentic workflows, integration of LLMs, model quantization, and custom analytics dashboards.

Question 2

How do you protect our intellectual property and data?

Accepted Answer

We sign comprehensive NDAs before code-level sharing. Our engineers configure AI systems to run locally, on private server instances, or with enterprise API terms (which exclude data from LLM training).

Question 3

Can your AI developers fine-tune models on our proprietary data?

Accepted Answer

Yes. Our ML engineers are experienced in fine-tuning open-source models (Llama, Mistral, Phi) using LoRA/QLoRA adapters on private datasets. They also handle training infrastructure on AWS SageMaker, GCP Vertex AI, or self-hosted GPU clusters.

Question 4

Do your AI engineers understand RAG architecture design?

Accepted Answer

Absolutely. Our specialists architect complete RAG pipelines — from document ingestion, chunking strategy, embedding generation, and vector storage (Pinecone, Qdrant, pgvector) to semantic retrieval, re-ranking, and LLM response synthesis. They also handle evaluation and hallucination reduction.

Question 5

How do AI/ML engineers integrate into product teams?

Accepted Answer

Our AI engineers work closely with product and backend teams. They expose model capabilities via clean FastAPI endpoints, document their APIs, write test harnesses, and collaborate on prompt versioning and evaluation pipelines. They treat model inference as an engineering system, not a research experiment.

Question 6

What is the typical timeline to start an AI project?

Accepted Answer

Due to the specialized nature of AI roles, match time is slightly longer at 3–4 business days. Once matched, our AI engineers typically deliver an initial proof-of-concept or architecture document within the first week so you can validate the approach before the full sprint begins.

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