AI Development.
Custom AI systems that survive contact with production.
Drema builds custom AI systems from problem definition through to production monitoring. Most AI projects fail at the boundary between a promising demo and a system real users depend on, so we design for that boundary first: evaluation harnesses, fallbacks, cost ceilings and human review paths before anything ships.
Custom AI systems that survive contact with production.
What usually
goes wrong.
Teams come to us after a proof of concept impressed everyone in a meeting and then fell apart in the hands of real users. The model hallucinated on edge cases nobody tested, costs scaled linearly with usage, and there was no way to tell whether a prompt change made things better or worse. The engineering around the model is what was missing.
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.
Feasibility and scoping
An honest assessment of whether AI is the right tool, what accuracy is achievable, and what it will cost per request at your volume.
Model selection and orchestration
Choosing and chaining the right models for each step, rather than routing everything to the largest and most expensive one.
Retrieval over your data
Chunking, embedding and retrieval tuned to your corpus so answers are grounded in your content, not the model's training data.
Evaluation harness
A regression suite of real cases so you can prove a change improved quality instead of guessing.
Guardrails and fallbacks
Input validation, output constraints, refusal handling and a defined behaviour for when the model is unavailable.
Production deployment
Latency budgets, caching, cost monitoring and observability that shows what the system actually did.
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
Problem framing
We define what correct looks like and how it will be measured before writing any code. Vague success criteria are the single biggest cause of failed AI projects.
- 02
Baseline and evaluation set
We assemble real examples with expected outputs. This becomes the yardstick every later change is measured against.
- 03
Prototype
The smallest system that produces a measurable score on the evaluation set, usually within two to three weeks.
- 04
Harden
Guardrails, cost controls, caching, error paths and the monitoring you need to operate it.
- 05
Ship and iterate
Deploy behind a flag, watch real usage, and feed failures back into the evaluation set.
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.
Document intelligence
Extracting structured data from contracts, invoices, claims and reports at a volume no team could read.
Natural-language interfaces
Letting people ask questions of a database or product in plain English, as we built into DeepSync.
Content generation at scale
Producing consistent, on-brand or curriculum-aligned material, as TestGenie does for exam papers.
Classification and routing
Triaging tickets, leads or documents to the right queue with a measured accuracy rate.
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.
How much does custom AI development cost?
Scope drives the cost far more than the AI does. A focused system on a well-defined problem typically runs a few weeks of engineering; a platform with retrieval, evaluation and multi-model orchestration is a longer engagement. We quote after a scoping call, and we tell you if the problem does not warrant AI at all.
Do you build with your own models or use existing ones?
We use existing frontier and open-weight models for the overwhelming majority of work, because training from scratch is rarely justifiable. The value we add is the retrieval, orchestration, evaluation and guardrails around the model, which is where projects usually fail.
How do you stop the AI from making things up?
Three things in combination: grounding answers in retrieved source content, constraining outputs to structures we validate, and an evaluation suite that catches regressions before release. We also design the interface so the system can say it does not know.
Can you work with our existing data?
Yes, and it is usually the point. We handle ingestion, cleaning, chunking and embedding of your documents, databases and APIs so the system reasons over your material rather than generic training data.
What if our data is confidential?
We work under NDA by default, can deploy into your own cloud account, and can use models with no-training data commitments or self-hosted open-weight models where the sensitivity requires it.
How long before we see something working?
A prototype scored against a real evaluation set typically lands within two to three weeks. That is deliberately early, because it tells us whether the approach is viable before significant money is committed.
Talk it through with a founder.
Bring the actual problem. You will get a straight answer on whether ai development is the right approach
— including when it is not.
