AI & Automation
AI features, assistants, knowledge bases built on your documents, and automation of routine work for growing teams.
The outcomeHours of manual work handed to software that checks its own answers.
What's included
We add AI features to new and existing products: assistants and chatbots that answer from your own documents, searchable knowledge bases (RAG), document processing and automation of routine work. Every project starts with one clear task to measure and a set of test questions, so you know how accurate it is before it reaches your customers.
AI features & assistants
Chat assistants that answer from your data, not with generic replies.
Knowledge bases (RAG)
Search and answers across your documents, with sources shown.
Workflow automation
Repetitive tasks across your tools done automatically.
Document processing
Pull out, sort and pass on data from PDFs, emails and forms.
AI inside existing apps
Add AI features to products you already run.
Accuracy tests & safety checks
Accuracy measured on sets of test questions, with safety checks in place.
How the project runs
Our AI & Automation process runs in 4 stages: Planning workshop, Prototype & test, Build & connect and Monitor & improve. Each stage ends with something you can review, such as a plan, a design, working software or a release, and nothing moves on until you have seen it and are happy with it.
- 01 / 04
Planning workshop
Pick the task, how to measure success and the data that matter most.
- 02 / 04
Prototype & test
A working prototype tested against a real set of questions.
- 03 / 04
Build & connect
The full build, with safety checks, logging and your systems connected.
- 04 / 04
Monitor & improve
Quality and cost tracked, with prompts and data improved over time.
Tools we use
For AI & Automation work we most often use OpenAI, Anthropic Claude, LangChain, LlamaIndex, pgvector and Python. We choose tools for your product rather than for our habits, and we stick to mainstream, well-documented technology that your own team, or any other agency, can maintain after handover.
- OpenAI
- Anthropic Claude
- LangChain
- LlamaIndex
- pgvector
- Python
- n8n
Related work
AI & Automation projects we've delivered, with what each client needed and what changed as a result. Every case study names the client's industry and country, and its numbers come from the client's analytics or our project records, not estimates.
ThinkRisk · Fintech · United States
Machine learning models for an autonomous finance and audit platform
ThinkRisk makes enterprise software that finds risk in financial transactions for internal auditors, controllers and finance teams. As a technical consultant, TrioFlare developed machine learning models for the platform.
- AI & Automation
Questions about AI & Automation
Straight answers to what clients usually ask before starting AI & Automation work with us: how it's planned, how long it takes, what you get at the end and how we work across time zones.
Will our data be used to train AI models?
No. We use AI services with settings that do not use your data for training. Where your data is sensitive, we keep it on your own servers.
How do you check that an AI feature really works?
Before we build, we collect real examples of the questions or tasks it must handle. Every time we release an update, we test it against those examples and measure how often it gets them right.
Standards & recognition
- MSME registeredRegistered with the Government of India under Udyam.
- Startup IndiaRegistered with Startup India, Government of India.
- Listed on ClutchOur profile, services and client reviews on Clutch.
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- Privacy policyWritten for the GDPR and India’s Digital Personal Data Protection Act.
Planning a ai & automation project?
Tell us what you're building. We reply within one business day.