:: AI services / Generative AI development
A generative AI development company for LLM apps you can trust
We build applications on large language models: assistants that answer from your documents, tools that write first drafts, and systems that turn messy text into clean data. The focus is accuracy, cost and keeping your data private.
RAG on your own data
Model-agnostic
Evaluation built in
:: What we do
Generative AI development services
Most generative AI projects are one of these patterns, or a mix of them.
01
Retrieval-augmented generation (RAG)
Search over your documents, wikis and records, with answers that cite their sources. The most common and most useful pattern.
02
Content and document generation
First drafts of proposals, reports, product descriptions and emails, in your format and voice, for a person to review.
03
Data extraction
Turn PDFs, emails and scans into structured fields your systems can use, with confidence scores and review queues.
04
LLM fine-tuning
Training a model on your examples when prompting and retrieval are not enough, or when a smaller, cheaper model can do the job.
05
Private and self-hosted LLMs
Open models such as Llama or Mistral running in your own cloud when data cannot leave your environment.
06
Evaluation and guardrails
Test sets, automatic scoring and filters that catch wrong, unsafe or off-topic answers before users see them.
:: Use cases
Generative AI use cases that work in business today
The projects that reach production share one trait: the model works from your own content and a person can check the result. These are the ones we see succeed most often.
Knowledge
Answer assistant on your documents
Staff or customers ask in plain English and get answers from your policies, manuals and help articles, with the source linked.
Read the blueprint
Operations
Document reading and extraction
Turn statements, forms, invoices and contracts into clean, checked data in your systems.
Read the blueprint
Insurance
Claim and case summaries
Summarise long files, emails and notes so a handler can decide in minutes instead of reading for an hour.
Read the blueprint
Support
Drafted customer replies
Draft answers from order data and policy, ready for an agent to check and send, or sent automatically for simple cases.
Read the blueprint
Sales and marketing
Proposals and product content
First drafts of proposals, product descriptions and emails in your tone of voice, built from your own templates and data.
Recruiting
Screening and matching
Read applications against a role’s real requirements and explain each ranking with evidence.
Read the blueprint
:: Side by side
RAG or fine-tuning?
The question we are asked most. The short answer: start with RAG, fine-tune only when tests say so.
RAG
Fine-tuning
Best for
Answers from documents that change often
A fixed style, format or narrow task
Keeps answers current
Yes, update the documents
No, needs retraining
Shows sources
Yes
No
Upfront effort
Lower
Higher: needs good training examples
Running cost
Depends on model size
Can be lower with a smaller tuned model
:: RAG vs fine-tuning
RAG vs fine-tuning: how to choose
Most business applications should start with retrieval (RAG). Fine-tuning earns its place in a narrower set of cases, and the strongest systems often use both.
Start here
Use RAG when answers must be current and traceable
The model looks up the relevant passages from your documents at question time and cites them. Updating knowledge means updating documents, not retraining. Best for policies, products, support and internal knowledge.
Specialise
Fine-tune when you need a style, format or skill
Training on hundreds or thousands of examples teaches a model a consistent tone, a strict output format or a narrow task such as classification. It does not keep facts up to date, so it rarely replaces RAG.
Combine
Use both for high-volume, high-accuracy work
A fine-tuned model that follows your format, fed by RAG with current facts. Worth it when volumes are large enough that a smaller fine-tuned model cuts running costs.
:: How it works
How we build generative AI applications
Generative AI is easy to demo and hard to make reliable. Our process is built around measurement.
01
Collect real examples
We gather questions, documents and good answers from your team to build the test set first.
02
Try models and methods
Several models and approaches are tested on the same set. We pick on accuracy, speed and cost.
03
Build the product
Retrieval, prompts, guardrails, user interface, access control and logging, ready for real users.
04
Measure in production
Live scoring, user feedback and cost tracking, with monthly improvements.
:: Quality and safety
How we test generative AI before anyone relies on it
A demo that works on five questions proves little. These are the checks every generative AI system we build goes through before launch, and keeps going through after it.
01
Build a test set from real questions
Collect a few hundred real questions or documents with the answers an expert would give. This set is the yardstick for every change to prompts, models or data.
02
Measure accuracy and groundedness
Score each answer for correctness and for whether it is supported by the source it cites. Answers that cannot be traced to a source are treated as failures.
03
Try to break it
Test with misleading, off-topic and adversarial inputs: attempts to extract data, override instructions or produce harmful content. Fix what gets through.
04
Check cost and speed
Measure cost per answer and response time at realistic volumes, and compare models. A cheaper model that passes the test set is usually the better choice.
05
Monitor in production
Log questions and answers, sample them for review every week, track cost and catch drift when documents or user behaviour change.
:: Cost and timeline
How much does generative AI development cost?
Typical prices for our projects, based on a senior team at $25 to $35 an hour. You get a fixed quote for each phase after a short call.
Prototype
$5k – $12k
3 to 4 weeks
One use case on one document set, tested against your real questions so you can see whether it is good enough before investing more.
Production app
$15k – $50k
8 to 14 weeks
Secure, monitored application with access control, integrations, an evaluation set and a handover to your team.
Platform or custom model
$50k – $120k+
3 to 6 months
Several use cases on shared infrastructure, fine-tuned or self-hosted models, and governance for company-wide use.
What it costs to run
Generative AI is billed per use. For most business apps, model usage runs from tens to a few thousand dollars a month depending on volume, document size and the model. Self-hosted models swap usage fees for GPU hosting costs. We measure the real cost per request during the prototype.
What moves the price
How many document sources and formats
Integrations with your systems
Accuracy and testing requirements
Fine-tuning or self-hosted models
Number of users, languages and channels
:: Why Sterling
How we keep generative AI safe and affordable
Your data stays in your cloud and is not used to train public models
Answers cite sources so people can check them
Automatic tests run on every change
Smaller models used wherever they pass the tests, to cut cost
Role-based access so people only see what they are allowed to
:: Tools we use
OpenAI
Anthropic Claude
Google Gemini
Llama
Mistral
LangChain
LlamaIndex
pgvector
Pinecone
Weaviate
AWS Bedrock
Azure OpenAI
4.9
Average rating on Clutch · 350+ projects since 2019
:: Questions
Questions about generative AI
How much does generative AI development cost?
Which LLM should we use?
What is RAG?
Can we keep our data private?
Do you also advise on generative AI strategy?
What are the most common generative AI use cases?
Should we use RAG or fine-tuning?
Can generative AI run on our own servers?
How do you stop the model making things up?
What does a generative AI app cost to run?
How long does a generative AI project take?
:: Related AI services
Other ways we can help
AI agent development
Agents that complete tasks inside Salesforce, ERP and email
AI chatbot development
Chatbots that answer from your own content
AI consulting services
Plan before you build: readiness, strategy and proof of concept
Machine learning development
Forecasting, scoring, NLP and computer vision
Send us your brief. We’ll help turn it into reality
A senior engineer replies within one business day with questions or a first plan: approach, team, timeline and estimate.