Question 1

How do you decide which LLM is right for our use case?

Accepted Answer

We evaluate four factors: required capability (reasoning depth, context window, multi-modal support), latency requirements, cost per token at your expected volume, and data privacy constraints. For most production workloads we run benchmarks with your real data before committing to a model — and we design the architecture to swap models without rewriting application logic.

Question 2

Should we use RAG or fine-tune a model on our data?

Accepted Answer

RAG is almost always the right starting point: it lets the model answer from live, up-to-date documents and gives users source citations. Fine-tuning is better when you need the model to adopt a specific style, terminology, or format that can't be captured in a prompt. The two approaches are also complementary — we often build RAG first and layer fine-tuning on top once the use case is validated.

Question 3

How do you handle sensitive or proprietary data during development?

Accepted Answer

We can work entirely within your own cloud environment using private deployments (AWS Bedrock, Azure OpenAI, or on-premise models), ensuring data never leaves your infrastructure. For cloud-hosted models, we implement PII redaction pipelines before any data reaches the API, and we review the data retention policies of every provider we recommend.

Question 4

How do you prevent AI hallucinations in production applications?

Accepted Answer

Hallucinations are reduced — not eliminated — through a layered approach: grounding outputs in retrieved documents via RAG, instructing models to cite sources or decline when uncertain, running automated factuality checks on outputs, and setting conservative temperature parameters. We also build human review queues for high-stakes outputs where zero hallucination tolerance is required.

Question 5

How quickly can we get a working GenAI prototype?

Accepted Answer

A focused proof-of-concept using your real data can typically be ready in 1–2 weeks. This gives you a genuine signal on quality and feasibility before any significant investment. We use fast prototyping to validate assumptions early rather than spending months on architecture upfront.

Question 6

How do you control the cost of LLM API calls in a production app?

Accepted Answer

We implement prompt caching for repeated context, response caching for identical queries, model tiering (using cheaper models for simpler sub-tasks), token budgets enforced per request, and real-time cost dashboards. These measures typically reduce API costs by 40–70% compared to a naive single-model implementation.

LLM-Powered Products That
Generate, Summarise & Reason

End-to-end generative AI services

LLM Application Architecture

RAG Pipelines over Proprietary Data

Prompt Engineering & Evaluation

Fine-Tuned Models for Brand Voice

Conversational AI & Chatbots

Document Intelligence

AI Copilots & Assistants

Multi-Modal AI

Summarisation & Translation

Real applications across every business function

Legal & Compliance

Knowledge Management

Customer Support

Content Creation

Data & Reporting

Developer Tooling

How we ship GenAI to production

Use Case Mapping

Prototype

Evaluation

Harden

Scale & Monitor

Ready to add generative AI to your product?

Our GenAI technology stack

What sets us apart

Production-first mindset

Evaluation-driven quality

Model-agnostic selection

Cost and latency engineering

Measurable business impact

Safety and compliance by design

Compliance Standards That Shape Our Generative AI Practice

Frequently Asked Questions

LLM-Powered Products That Generate, Summarise & Reason