I want to use AI properly
AI that does a real job in your product, not a chatbot nobody uses.
People usually mean things like: Answer questions from my own documents · Summarise what my customers write · An assistant inside my product
Start here
Whole projects. Answer a few questions, see a real range, then get quotes from developers who have done it before — no account needed to ask.
Or a specific job, if you already know what you need
20 fixed pieces of work, each with a scope and a price. These assume you already have something to work on.
Token budget and caching layer for your AI feature
Cut your model spend with caching, routing and per-user budgets, without changing what your users experience.
Harden your LLM feature against prompt injection
Close the gaps that let a crafted input make your agent leak data, call the wrong tool or ignore its instructions.
MCP server so agents can use your internal tools
Expose your internal APIs to coding and support agents safely, with scoped permissions and a full audit trail.
Streaming chat UI with tool calls and real error states
The front end your AI feature deserves: token streaming, cancellation, retries and messages that survive a refresh.
Evaluation harness so prompt changes stop regressing
A test suite for your AI feature that runs in CI and tells you when a prompt or model change makes things worse.
Make your RAG bot stop inventing answers
Diagnose why retrieval misses, then fix chunking, reranking and prompting until answers are grounded and citable.
Moderation for user-generated text and images
Automatic screening of uploads and posts with tunable thresholds, an appeals path and a queue for the grey area.
Meeting transcription with searchable summaries
Calls transcribed with speaker labels, summarised into decisions and actions, and searchable across every past meeting.
Replace keyword search with semantic search
Search that understands intent, with hybrid ranking so exact matches still win when users type an exact term.
Agent that triages support before you wake up
Incoming tickets classified, tagged, prioritised and drafted overnight so your morning starts with replies to approve.
Turn messy documents into structured data
Contracts, CVs or reports converted into validated JSON you can query, with a review queue for low-confidence fields.
RAG chatbot trained on your documentation
A support assistant that answers from your docs, cites its sources and says it does not know when it does not.