Generative AI & LLM Applications.
LLM products with grounded answers and controlled costs.
Drema builds generative AI applications on large language models: retrieval-augmented assistants, copilots embedded in existing products, and content generation systems. The engineering that matters is retrieval quality, prompt versioning, structured output validation and per-request cost control — the parts that decide whether the product is trusted or quietly abandoned.
LLM products with grounded answers and controlled costs.
What usually
goes wrong.
The demo is easy and the product is hard. Answers drift off-source, costs balloon once real traffic arrives, one prompt tweak silently breaks three other behaviours, and nobody can measure whether today's version is better than last week's.
Everything that ships
with this work.
Not a menu to choose from. Each piece is here because leaving it out is what makes this kind of project fail six months later.
Retrieval-augmented generation (RAG)
Ingestion, chunking, embedding and retrieval tuned so answers cite your actual content.
Prompt architecture and versioning
Prompts treated as versioned code with tests, not strings buried in a file nobody dares touch.
Structured output validation
Schema-constrained responses so downstream systems can rely on the shape of what comes back.
Streaming chat interfaces
Responsive UI with token streaming, cancellation, history and citation display.
Cost and rate control
Caching, model tiering and per-user budgets so a spike in traffic is not a spike in invoice.
Evaluation and tracing
Golden-set scoring plus request tracing to debug why a specific answer went wrong.
The order matters more
than the tools.
Most of what separates a project that lands from one that stalls is sequence. This is the order we work in, and why each step comes where it does.
- 01
Define the answer
We write out what a great response looks like for twenty real questions. That set becomes the specification.
- 02
Get retrieval right
Most bad LLM answers are retrieval failures, not model failures. We tune this before touching prompts.
- 03
Build the interaction
Streaming, citations, follow-ups and the affordances that let a user recover when an answer is wrong.
- 04
Constrain and measure
Guardrails, structured outputs, cost ceilings and scoring against the golden set.
- 05
Release and observe
Ship to a subset, trace real conversations, and expand the evaluation set from actual failures.
Where this gets
put to work.
The situations this service is built for. If one of these sounds like your problem it is worth a conversation — and if none of them do, say so on the call and we will point you at what would actually fit.
Ask-your-data assistants
Querying analytics or operational data conversationally, as DeepSync's AI chat does over session behaviour.
Internal knowledge copilots
Grounded answers over policies, runbooks and documentation, with citations back to source.
Curriculum and content generation
Structured, standards-aligned generation like TestGenie's question papers and answer keys.
Drafting and summarisation
Turning long documents, calls or threads into reliable summaries a professional will sign off.
Have a use case that is not on this list? That is usually the interesting one.
Chosen to fit,
not to impress.
We pick tools that suit the problem and that your team can maintain after we hand over — never to pad a capability list.
Questions we
get asked.
Straight answers, including the ones that talk you out of work we would otherwise be paid for.
What is RAG and do we need it?
Retrieval-augmented generation means fetching your relevant content first and giving it to the model as context, so answers are grounded in your material. You need it whenever answers must reflect your specific documents, policies or data rather than the model's general knowledge — which is most business use cases.
Which LLM should we use?
It depends on the task, the latency you can tolerate and your cost ceiling. We often tier: a smaller model for routing and simple steps, a frontier model for the hard reasoning. We benchmark candidates on your evaluation set rather than choosing on reputation.
How do you control LLM costs?
Caching repeated work, routing easy requests to cheaper models, trimming context aggressively, and enforcing per-user and per-org budgets. We instrument token spend per feature so you can see exactly what each capability costs.
How do you prevent the assistant from answering things it should not?
Scope enforcement in the system prompt, retrieval restricted to permitted content, output validation, and explicit refusal behaviour. We also test adversarial cases as part of the evaluation set.
Can it work with our permissions model?
Yes, and it must. Retrieval is filtered by the requesting user's access rights so the assistant can never surface a document that user could not open directly. This is a common oversight in first-generation internal copilots.
How do you know a prompt change actually helped?
Every change is scored against a fixed golden set of real cases before release. Without that, prompt engineering is guesswork and regressions ship invisibly.
Talk it through with a founder.
Bring the actual problem. You will get a straight answer on whether generative ai & llm applications is the right approach
— including when it is not.
