Applied AI Engineering
Practical AI features built into your business software — not standalone experiments. Automate decisions, summarize documents, extract structured data, and augment your team's work with AI that connects to your actual systems.
The real challenge
Most businesses have seen AI demos — a chatbot that sounds impressive, a document that gets summarised, a question that gets answered. The hard part is not the demo. It is building AI that reliably connects to your actual data, handles edge cases, and delivers consistent value in daily operations.
Generic AI chatbots have no knowledge of your business. They do not know your product catalogue, your customer history, your internal policies, or your operational data. Ask them a question about your own business and they will either hallucinate an answer or admit they do not know — neither of which helps your team.
LLM outputs that are not grounded in your actual documents or database cannot be trusted at scale. An AI that invents a contract clause or quotes the wrong pricing is not a productivity tool — it is a liability. Production AI requires retrieval systems, output validation, and fallback paths for when confidence is low.
Upotto's approach is integration engineering, not AI experimentation. We design AI features that connect to your real data, operate within defined boundaries, and produce outputs your team can act on — rather than outputs that need to be fact-checked before anyone trusts them.
What we build
Answer questions from your actual business documents — policies, product specs, contracts, historical records.
Extract structured data from contracts, invoices, and reports — fields, dates, amounts, entities.
Condense long reports, communication logs, and transaction histories into concise, actionable summaries.
Semantic search across business knowledge bases — find relevant content even without exact keyword matches.
Auto-draft messages, reports, and summaries within defined templates, reducing repetitive writing work.
Embedded AI analysis within existing software — surface relevant context at the moment a decision is made.
Convert unstructured text inputs — emails, scanned forms, free-text fields — into clean, database-ready records.
Natural language query interfaces for internal tools — ask your data a question in plain language.
How it works
Retrieval-Augmented Generation (RAG) is the architecture that makes AI genuinely useful for business applications. Instead of asking an LLM to answer from its training data, RAG first retrieves relevant sections from your documents or database, then provides those sections as context to the model before generating a response.
The result: AI answers grounded in your specific documents — company policies, product specs, transaction records, contracts — rather than generalised knowledge that may not apply to your situation. When the AI does not have relevant information, it says so, rather than filling the gap with a plausible-sounding invention.
We design RAG pipelines end-to-end: document ingestion and chunking, embedding generation, vector storage, semantic retrieval, and context injection into the LLM prompt. The pipeline stays current as your documents are updated.
Document ingestion
Your documents are processed, split into semantically coherent chunks, and stored.
Embedding generation
Each chunk is converted to a vector embedding — a numerical representation of its meaning.
Semantic retrieval
At query time, the user question is embedded and matched against your document vectors to find the most relevant chunks.
Grounded generation
The retrieved chunks are supplied to the LLM as context. The model answers using your actual content, not its training data.
Capabilities
Connect your application to OpenAI, Anthropic Claude, Google Gemini, or other LLM providers — with proper authentication, error handling, and retry logic.
Design and implement full retrieval-augmented generation pipelines: document ingestion, chunking strategy, embedding generation, vector storage, and retrieval at query time.
Configure and maintain vector databases for semantic search, including embedding pipelines that keep your knowledge base current as documents change.
Design system prompts and few-shot examples that produce reliable, well-structured outputs — and iterate based on real failure cases from your data.
Implement schema validation, content checks, and fallback paths so AI outputs are verified before they reach users or trigger downstream actions.
Implement token-by-token streaming for chat interfaces and long-form generation, so users see output as it arrives rather than waiting for full completion.
Embed AI capabilities into existing web and mobile applications as API-backed features — no need to rebuild your core software stack.
Process documents, classify records, or enrich data asynchronously using background job queues — AI work that does not need to happen at request time.
Manage prompt length, model selection, caching, and batching to keep LLM API costs proportionate to the value the feature delivers.
Use cases
ERP & business software
An ERP system that summarises a dealer's full transaction history — credit, orders, collections, returns — on demand, in plain language, for a sales manager reviewing an account.
Document-heavy industries
Legal and finance teams uploading contracts or statements and receiving extracted structured data — parties, dates, amounts, obligations — without manual re-entry.
Internal knowledge assistants
Staff asking questions of company policy documents, product manuals, or onboarding materials and getting direct answers grounded in the actual source text.
Sales workflow tools
Sales software that auto-drafts follow-up messages based on past interaction history and deal stage — reviewed and sent by the rep, not fully automated.
Operations monitoring
Operations dashboards that use AI analysis to flag anomalies or notable patterns in inventory, order volume, or logistics data before they become problems.
Customer support tooling
Support agent interfaces where AI generates a suggested response based on the customer query and knowledge base — the agent reviews and sends, cutting handle time.
Technology
We use LLM APIs from established providers — not custom-trained models. This is the right approach for most business AI: faster to ship, lower cost, and easier to maintain. The differentiation is in how well the integration is engineered, not in the model itself.
LLM APIs
OpenAI · Anthropic Claude · Google Gemini
Vector Databases
Embedding pipelines · Semantic retrieval
NestJS
AI feature backend · Job queues · API layer
PostgreSQL
Structured data storage · Query grounding
Next.js
Streaming UI · Chat interfaces · AI-powered pages
Docker
Reproducible AI service deployment
How We Work
A structured approach that keeps projects on track without unnecessary ceremony.
We learn how your business operates, what the current pain points are, and what success looks like. No assumptions.
Define the technical scope, system architecture, data model, and integration touchpoints before writing a line of code.
Design interfaces that your actual users — operators, managers, field staff — can navigate without training.
Iterative builds with regular check-ins. You see working software early, not just status reports.
Functional, integration, and edge-case testing. We fix issues before they reach your users.
Production deployment with proper environment configuration, data migration support, and go-live assistance.
Post-launch monitoring, bug fixing, and feature iterations as your operations evolve.
Why Upotto
AI features are scoped around specific workflow problems — document extraction, summarisation, search — not added as a checkbox. If AI is not the right tool for a part of your system, we will say so.
LLMs hallucinate, drift, and fail in predictable ways. We design systems with guardrails, retrieval grounding, and validation layers that account for those failure modes rather than ignoring them.
AI features connect to your existing backend and database — not as a separate bolt-on system that requires its own maintenance overhead. Your team gets AI capabilities without a parallel infrastructure to manage.
By grounding AI responses in your actual documents and records, we reduce the risk of the model generating responses that contradict your real data. Responses that cannot be supported by retrieved content are flagged rather than fabricated.
Related Services
The business software system that AI features get built into — web systems, dashboards, and workflow tools.
Explore serviceThe backend APIs and processing pipelines that handle AI feature integration and data flow.
Explore serviceSaaS products where AI features become a core part of the product value proposition.
Explore serviceFAQ
Tell us about the workflow you want to improve. We will work out which AI capabilities make sense, what the integration looks like, and what realistic outcomes to expect.
