Services / LLM integration
LLM features your users can rely on.
Knowledge search, document understanding, summarisation and structured extraction, built into your product or internal tools, connected to your data and measured.
Where it fits
Common starting points.
Search
Answers from company knowledge
Retrieval over your documents with citations, permissions respected.
Documents
Understanding and extraction
Turn contracts, invoices or forms into structured, validated data.
Product
AI features for your users
Summaries, drafting and assistants inside the product, with guardrails.
How we build
Measured before it ships.
Every LLM feature starts with an evaluation set built from your real cases, so quality and cost are known before users see it.
- → evaluation set from your own data and edge cases
- → retrieval and prompt design, versioned
- → structured outputs validated against a schema
- → latency and cost budgets per request
- → monitoring and regression checks after launch
Deployed where your data lives
Your AI runs inside the cloud you already trust.
We pick the platform around where your data, identity and security controls already are.
For teams on Microsoft identity and Azure data services.
For data in BigQuery and workloads on Google Cloud.
For agents that stay inside your AWS accounts and VPCs.
For a managed agent runtime when it fits your controls.
Proof
Shipped AI, not slides.
Our product · AI
TradeMindAI
AI-assisted technical analysis for stocks, crypto and forex. Our own product, live on iOS.
Read the case study →Our own operation
How we run ByteandRise
This site and our marketing are run by a team of agents with a quality gate and a human approving what ships.
See how it works →How we engage
Start small. Earn the next step.
Pick the workflow worth automating
Interviews, a data and integration audit, a ranked use-case list and a build plan with running costs.
Prove it on your data
One workflow, connected to real systems, measured against criteria agreed up front.
Ship it properly
Integrations, evaluation, monitoring, security review steps, documentation and handover.
Keep it working
Evaluation runs, model and provider updates, cost control and a monthly report.
Questions
What buyers ask us first.
Where does our data go?
It stays in the cloud setup you already use. We build on Azure AI Foundry, Google Vertex AI, AWS Bedrock or Claude Platform, chosen around where your data, identity and security controls already live.
Which model do you use?
The one that meets your quality, cost and data-residency needs on the platform you already use. We compare candidates on your evaluation set.
What does it cost to run?
Every scoping sprint ends with a cost model for both the build and the running costs. Systems we ship come with per-agent cost tracking and hard spending caps.
Book an AI scoping call
Bring one workflow. We'll tell you if an agent should run it.
or write to contact@byteandrise.com · we work under NDA