AI solutions
AI that works with your business data
Agentic RAG assistants, AI agents and AI-powered product features — engineered for accuracy, security and real business outcomes.
- Agentic RAG
- AI agents & automation
- Permission-aware & secure
How we build
AI-accelerated delivery, engineered by experts
We use the best AI engineering tools in every project — so you get production-quality software sooner, with senior engineers reviewing every line.
Our AI engineering toolkit
Claude
Agentic coding, architecture reviews and test generation
Cursor
AI-native IDE for fast, context-aware development
GitHub Copilot
In-editor pair programming across the team
Stitch
Rapid UI exploration from ideas to screens
Agentic RAG systems
AI assistants that plan, search across your data and tools, and answer with citations.
AI agents & automation
Workflow agents that handle support triage, document processing and back-office tasks.
AI features in your product
Visual AI, recommendations, semantic search and copilots built into web and mobile apps.
Human-reviewed, secure by default
Every AI-assisted change goes through code review, automated tests and security checks.
Capabilities
What we build
From quick proofs of concept to production AI systems.
Agentic RAG assistants
Assistants that search documents, databases and APIs, reason across them and answer with citations.
Customer support copilots
Resolve customer questions using order, account and policy data — with smooth human handover.
Document intelligence
Extract, classify and summarise contracts, invoices, forms and technical manuals.
Workflow automation agents
Agents that triage tickets, update CRMs and draft responses inside your existing tools.
AI features for your app
Semantic search, recommendations, visual AI and copilots inside web and mobile products.
Evaluation & monitoring
Test suites, guardrails and dashboards that keep AI quality, cost and latency in check.
Process
How an AI project runs
A low-risk path from idea to production.
01 Discover
Pick a high-value use case, map the data sources and define success metrics.
02 Prototype
A working proof of concept on your real data within weeks.
03 Harden
Access control, evaluation suites, guardrails and integration with your systems.
04 Operate
Monitoring, feedback loops and continuous improvement on AWS.
Success stories
AI in production
Real products in production — from booking platforms to AI-powered commerce.
Pi Play – Padel Booking & Community App
A booking management app that simplifies court booking for padel players and club owners, and builds a community where players connect, compete and grow together.
Read case study
Max Bazar – Food & Grocery Delivery Platform
A franchise-based platform that streamlines operations between restaurants, grocery stores and delivery personnel through two mobile apps and a central admin panel.
Read case study
Fashion Mate AI – AI-Powered E-commerce Ecosystem
A complete e-commerce ecosystem with a super-admin console, a seller admin for marketplace vendors, and web and mobile storefronts with AI-driven visual try-on and styling advice.
Read case studyFAQ
AI solutions FAQ
What is Agentic RAG?
Agentic RAG (retrieval-augmented generation) is an AI system that plans its own steps, chooses tools such as document search, SQL or APIs, checks the results and then answers with citations. It handles multi-step questions that span several systems.
Is our data safe?
Yes. We apply your existing access rules to retrieval, keep data in your cloud account where required, encrypt it in transit and at rest, and can use model providers and settings that do not train on your data.
How long does an AI proof of concept take?
Typically two to four weeks for a focused use case with available data, followed by a hardening phase before production rollout.
Which AI tools does TechieBears use?
We use Claude, Cursor, GitHub Copilot and Google Stitch to accelerate design and development, and we build client AI systems with leading foundation models, vector databases and AWS services.
Have an AI idea?
Let us help you find the use case with the best return — and build it properly.