Use-case and architecture design
Selection of the right approach — prompt engineering, retrieval-augmented generation or fine-tuning — based on the specific accuracy and cost requirements of the use case.
Generative AI
Generative AI Development for Production Use
We design and build generative AI applications on large language models — assistants, copilots and content generation tools — engineered for accuracy, cost control and real production traffic, not just a demo.
From impressive demo to reliable production feature
Generative AI prototypes are quick to build and easy to be impressed by, but production use introduces problems a demo never surfaces — hallucination risk, cost at scale, latency and the need for consistent, auditable outputs.
- A prototype that worked well in testing but hallucinates on edge cases in production
- No retrieval layer, so the model answers from outdated or generic training data
- Unpredictable API costs once usage scales beyond a pilot
- No evaluation framework to measure output quality over time
- Sensitive data sent to a third-party model without proper controls
Engineered for accuracy, cost and scale
We architect generative AI applications with retrieval-augmented generation against your own data, evaluation frameworks to catch quality regressions, and cost and latency engineering so the application performs reliably once it's carrying real traffic, not just a demo audience.
What's included
Service components
Retrieval-augmented generation (RAG) pipelines
Your own documents, product data or knowledge base indexed and retrieved at query time, so answers are grounded in current, accurate source material.
Prompt engineering and evaluation
Structured prompt design paired with an evaluation framework that scores outputs for accuracy and consistency before and after any change.
Application and interface development
Chat interfaces, internal copilots or embedded generation features built around real user workflows.
Cost and latency engineering
Model selection, caching and request design tuned to keep response time and API cost predictable at production volume.
Guardrails and safety controls
Input and output filtering, data handling controls and escalation paths for anything the model shouldn't answer confidently.
Process
How we deliver it
- Step 01
Define the use case
Clarify the exact task, acceptable error tolerance and the data the model will draw from.
- Step 02
Architect
Choose between prompt engineering, RAG or fine-tuning based on accuracy, cost and data requirements.
- Step 03
Build
Develop the retrieval pipeline, application interface and evaluation framework together.
- Step 04
Evaluate
Test against a defined set of real queries, scoring for accuracy, tone and failure modes.
- Step 05
Deploy and monitor
Launch with cost and quality monitoring, and a process for updating the underlying knowledge base.
Outcomes
What you get out of it
Grounded, current answers
Retrieval against your own data reduces hallucination compared with a model working from training data alone.
Predictable running cost
Engineering choices made with production-scale usage in mind, not just a demo.
Measurable output quality
An evaluation framework that catches regressions before users do.
A feature users actually adopt
Interfaces designed around real workflows rather than a generic chat window.
Why D2S
Why teams choose us for this
- We build for production reliability, not just an impressive prototype
- Retrieval-augmented approaches used by default to ground outputs in your own data
- Cost and latency engineered in from the architecture stage, not fixed after launch
- Evaluation and monitoring included so quality is measured, not assumed
Industries
Sectors we work with
SaaS & TechnologyBFSIHealthcare & ClinicsEducationPublishers & MediaProfessional Services
FAQ
Frequently asked questions
01What is generative AI development?
Generative AI development is the engineering work of turning a large language model into a reliable application feature — including retrieval of your own data, prompt design, evaluation and cost or latency engineering — beyond simply calling a model API directly.
02What is retrieval-augmented generation (RAG)?
RAG is an architecture where the model retrieves relevant information from your own documents or database at query time and generates its answer grounded in that retrieved content, which significantly reduces hallucination compared with relying on the model's training data alone.
03Do you build on our own model or a third-party one?
Most applications are built on established third-party foundation models via API, layered with retrieval, prompt engineering and guardrails specific to your data; fully custom model training is reserved for cases that genuinely require it.
04How do you prevent the model from giving wrong answers?
Through retrieval grounding, an evaluation framework tested against real queries, and guardrails that route uncertain or out-of-scope questions to a human rather than letting the model guess.
05What does this cost to run at scale?
It depends on model choice, query volume and retrieval complexity; we engineer for predictable cost by choosing appropriately sized models and caching wherever it doesn't compromise accuracy.
06Can this integrate with our existing product or internal tools?
Yes, generative AI features are typically built as an integrated part of an existing product or internal tool rather than a standalone application.
Keep exploring
Related services
Scope a generative AI feature
We'll review your use case and data and recommend the right architecture — prompt engineering, RAG or fine-tuning.