Question 1

How do you ensure AI answers are accurate and trustworthy?

Accepted Answer

To ensure accuracy, we primarily use Retrieval-Augmented Generation (RAG), which grounds the model's responses in your own verified documents and data. This includes providing citations so users can trace answers back to the source. We also use automated evaluation frameworks to test for correctness and guard against hallucinations.

Question 2

What is "MLOps" and why is it important?

Accepted Answer

MLOps (Machine Learning Operations) automates and productizes the ML lifecycle, from data prep to deployment and monitoring. It prevents model degradation by handling drift, versioning, tracking, and safe rollouts, keeping AI systems reliable over time.

Question 3

What kind of data do we need to provide?

Accepted Answer

For RAG systems, a corpus of verified documents (like PDFs or website content) is needed. For predictive models, structured data such as sales history or user activity is key. We audit existing sources and help establish labeling and collection pipelines if required.

Question 4

How do you handle privacy and sensitive data?

Accepted Answer

We use PII redaction, encryption at rest and in transit, and role-based access controls. Deployments can be hosted in your private cloud with GDPR and SOC2-compliant providers, ensuring your data is never used for training.

Question 5

What's the difference between RAG and fine-tuning?

Accepted Answer

RAG provides knowledge at query time for transparency and easy updates. Fine-tuning modifies model weights to teach new skills or formats. We usually use RAG first for cost-effectiveness and reserve fine-tuning for specific behavioral needs.

Question 6

How do you control and predict AI costs?

Accepted Answer

We use caching, efficient smaller models, rate limits, and budgets. Dashboards track token usage and cost per transaction in real time, helping you make informed scaling decisions.

Question 7

How fast can we launch a first version (MVP)?

Accepted Answer

A pilot like an internal Q&A bot can typically be delivered in 2–4 weeks. This covers scoping, data pipeline setup, prototyping, and initial safety checks. We then iterate and harden for production.

Question 8

Can you integrate AI with our existing apps and APIs?

Accepted Answer

Yes. We build secure AI agents that work with your tools, like Jira, Salesforce, or databases. All actions use strict permissions, logging, and optional user confirmations for critical tasks.

Question 9

How do you evaluate model quality and performance?

Accepted Answer

We combine automated metrics (like precision/recall), LLM-as-a-judge evaluations, and human audits. We also track cost and latency, ensuring each update delivers measurable improvements.

Question 10

Do we need a large, expensive model like GPT-4?

Accepted Answer

Usually no. We prioritize smaller, faster, cheaper open-source models with RAG and smart prompting to achieve strong performance without flagship model costs.

Question 11

What happens if a model's performance degrades?

Accepted Answer

This is called model drift. We use automated monitoring to detect drops in accuracy or data shifts. Alerts trigger re-evaluation and retraining pipelines to restore performance.

Question 12

How do you align the AI with our brand's tone of voice?

Accepted Answer

We use prompt engineering to define persona, tone, and rules. If needed, we fine-tune on your existing communications so the AI speaks in your brand's voice.

Question 13

Who on our team needs to be involved in an AI project?

Accepted Answer

We typically collaborate with a product/business owner, subject-matter experts, and your technical team. We handle the ML and MLOps work while keeping stakeholders aligned.

Question 14

Do you provide training and documentation?

Accepted Answer

Yes. We deliver thorough documentation on architecture, pipelines, and ops, plus role-based training so your team can confidently manage and extend the solution.

Question 15

What does ongoing support look like after launch?

Accepted Answer

We offer support retainers that include incident response, performance reviews, drift monitoring, and retraining. The level of support is tailored to your needs.

Question 16

Do we own the intellectual property (IP)?

Accepted Answer

Yes. You own all data, models, and code developed for your project. We hand over all repos and deliverables to give you full control—no lock-ins.

Question 17

How is your pricing structured for AI projects?

Accepted Answer

We start with a fixed-fee discovery and pilot phase. Ongoing development typically runs in agile sprints, and we also offer monthly retainers for continuous improvement and MLOps.

AI & Machine Learning That Ships Business Value

Why Build AI with Webnith

Product-Led Approach

Guardrails & Governance

MLOps That Sticks

Our AI/ML Services

Retrieval-Augmented Generation (RAG)

NLP & Chatbots

Computer Vision

Recommendations

Fine-Tuning & Custom Models

MLOps & Observability

How We Work

Discovery & Framing

Data & Prototyping

Hardening & Safety

Deploy & Improve

Frequently Asked Questions

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