from$0
AI Product Build. Fixed price after scoping.
Dappasol / What we build / AI Integrations & Automation
AI in your product that survives.
LLM features inside your product. Retrieval over your own data. Evals and guardrails around all of it. Built by engineers who ship to production, not a prompt in a box.
flat$0
Week-1 Build Audit. Credited against the build.
from$0/mo
Scale Retainer. Evals watched, features shipped.
Why most AI features die01 / RISK
The demo works. Production is where AI goes to die.
A slick prompt looks like magic in a meeting. Then it meets real data, real users and real load. Here is what usually breaks:
01The demoWorks once, in a meeting
02Real dataHallucinates without grounding
03No evalsNobody can say if it got worse
04RetrievalRAG that returns the wrong page
05Cost + latencyFine at 10 users, ugly at 1,000
What we build02 / SCOPE
Four kinds of AI that earn their keep.
01
LLM features in your product
Drafting, summarising, extraction, classification, copilots: model calls living inside your UI and your backend, doing a job your users pay for. Scoped to the feature, not to the hype.
02
RAG over your data
Answers grounded in your docs, tickets and records, with citations. The retrieval index is built on your data and belongs to you.
03
Evals + guardrails
A test suite for the model: accuracy measured before launch, re-run on every change, with guardrails on what the model is allowed to do and alerts when quality drifts.
04
AI on your ops
Agents doing multi-step work inside your stack: triage, enrichment, follow-up. When the job is more ops than product, that is its own lane.
Internal automations →The offer03 / BUILD
The AI Product
Build.
We scope the use case and write the eval spec first, build the feature or retrieval system, integrate it into your product and data, then deploy it monitored. One senior team, fixed scope, from $8,900.
Works against your
real data, or we
keep building free.
real data, or we
keep building free.
If it doesn't perform on your real workflows by the date we set, we keep working at no cost until it does. Scope locked week one.
What's includedValue
Use-case scoping & eval specwhat "good" means, written down first
$3,000
LLM feature / RAG buildthe working system, in your product
$18,000
Integration with your product & dataUI, backend, docs, records
$4,000
Evals + guardrails + monitoringso you can trust it after launch
$2,000
100% code & IP ownershipprompts, evals, index: your repos
Yours
Bonus: 30-day supportwe're on call
$1,000
Your fixed price
total value $28,000from $8,900
Scope my use case →
total value $28,000from $8,900
How it works04 / PROCESS
01
Scope
+ eval spec
We map the task, the data and the systems it touches, then write down what "good" means as an eval spec. You get a fixed scope and price before we build.
02
Build
+ integrate
Senior engineers build the feature or retrieval system and wire it into your product and your data, with guardrails and the eval suite running from day one.
03
Deploy
+ monitor
It ships to production monitored, with alerts when quality, cost or latency drifts. Documented and entirely yours. We are on call for 30 days after go-live.
Questions05 / FAQ
Before you ask.
Which models and tools do you use?
Whatever fits the job: Claude, GPT and open models, orchestrated with the right framework and tool calls. We pick for accuracy, cost and latency on your use case, not for hype, and we are not locked to one vendor.
How do you stop hallucinations?
Retrieval grounded in your own data, guardrails around what the model is allowed to do, and an eval suite that runs on every change. The feature stays inside defined actions, cites its sources where it matters, and we measure accuracy before anything goes live.
Will it survive production?
That is the whole point of the eval-first approach. Every build ships with an eval suite, monitoring, alerting on drift, and cost and latency budgets set up front, so the feature that worked in the demo keeps working at real load on real data.
Does my data end up training someone's model?
No. Your data stays in your accounts and your infrastructure. We use API and deployment options where your data is not used for training, and the retrieval index we build over your data belongs to you.
Do I own it?
100%. The code, the prompts, the eval suite and the infrastructure land in your repositories and your accounts. Zero lock-in, written into the engagement from day one.
What does it cost?
The AI Product Build starts from $8,900, fixed price set after we scope the workflow and the data. No hourly surprises. The $500 Week-1 Build Audit maps the use case first and is credited against the build.
Free, no-obligation
Ship AI you can
stand behind.
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