Our work
Agentic AI & the Intelligent Business
Custom software built on LLM foundations, AI agents that take real action, and the internal tooling that puts your team's knowledge to work.
The most important shift in software right now isn't a new framework or a better database. It's the ability to build systems that can reason, plan, and act — not just retrieve. Agentic AI is software that doesn't stop at answering a question. It takes the next step: looks something up, makes a judgment call, uses a tool, and follows through. For most businesses, that's the difference between a chatbot that costs money and an autonomous system that makes money. We build the second kind.
LLM-native builds
Not a chatbot wrapper
RAG + agents
Grounded in your data
GCP & AWS
Production-grade infrastructure
What agentic AI actually means in practice
An LLM on its own is a powerful tool for generating text. An agent is an LLM connected to the world: it can search your knowledge base, query your database, send an email, update a record, or call a third-party API — and it can do all of that in a sequence, making decisions along the way based on what it finds.
For a commerce business, that might be an agent that monitors incoming orders, flags the ones that need attention, drafts the customer communication, and routes the rest to fulfilment — with a human approving the edge cases. For a services firm, it might be an internal assistant that can answer questions about policies, retrieve client records, draft proposals, and schedule follow-ups. The common thread: real work gets done, not just text generated.
Agentic commerce and e-commerce intelligence
The next wave of e-commerce is agentic. Shoppers are already using AI assistants to research and buy. Search engines are shifting from link lists to synthesised answers. Recommendation engines are becoming reasoning engines. Businesses that adapt their digital presence and operations for this shift will have a compounding advantage over those that don't.
We help commerce businesses get ready. That means AI-powered product discovery and search, recommendation systems that understand context rather than just history, dynamic pricing intelligence, inventory and demand forecasting agents, and the structured data and metadata that make your products visible to AI-driven shopping interfaces.
It also means building the agentic layer into your operations: an agent that watches your ad spend, one that monitors your competitive landscape, one that drafts and schedules your content calendar. Commerce at scale with a smaller, sharper team.
- AI-powered search and product discovery
- Contextual recommendation systems
- Structured data for AI shopping agents
- Ad and content optimization automation
- Demand and inventory forecasting
Internal AI assistants for your team
One of the highest-ROI applications of LLMs isn't customer-facing at all — it's giving your employees a private, secure AI assistant that knows your business. Connected to your documentation, your CRM, your support history, your product catalogue — an assistant that can actually answer internal questions accurately rather than hallucinating from training data.
We build these systems with retrieval-augmented generation (RAG): your documents and data are indexed and surfaced at inference time, so the model answers from your actual knowledge, not its general training. The result is an assistant your team trusts because it's grounded in your specific information.
We also build agentic workflows that automate internal processes: document review, compliance checking, data extraction from unstructured inputs, report generation, onboarding pipelines. Work that used to take hours gets done in seconds, with a human reviewing the output rather than doing the underlying work.
- Private, domain-grounded internal AI assistants
- Retrieval-augmented generation (RAG) over your documents and data
- Automated document review and extraction
- Onboarding and compliance workflow automation
- Slack, email, and tool integrations for agentic workflows
LLM-native software and the architecture underneath
Building reliable AI products requires more than wrapping an API call. Prompt engineering matters, but so does the evaluation framework that tells you whether your prompts are working. So do the guardrails that keep the system from going off-track. So does the infrastructure that handles latency, rate limits, cost, and observability at production scale.
We build LLM-native products from the foundation: model selection, prompt architecture, tool definitions, agent orchestration, evaluation harnesses, and the deployment infrastructure on GCP or AWS. We use the best models for each task rather than defaulting to the biggest one, and we build the system to be model-agnostic so you can upgrade as the landscape evolves.
Every AI system we build has a defined scope, a clear human oversight model, and logging that lets you understand what it did and why. Explainability and trust are built in, not added on.
- Model-agnostic architecture (Anthropic Claude, OpenAI, Gemini, open-source models)
- Agent orchestration and multi-step reasoning
- Tool and function calling integrations
- Prompt management and evaluation frameworks
- Observability, cost controls, and audit logging
What this looks like
Things we build in this space
AI-Powered Internal Knowledge Base
A private internal assistant for a professional services firm, built on RAG over their policy library, past proposals, and client records — giving every team member accurate, sourced answers in seconds.
Agentic Order Processing System
An autonomous order triage system for an e-commerce operation: an agent that reads incoming orders, flags exceptions, drafts customer communications, and routes clean orders to fulfilment — reducing manual review time dramatically.
AI-Driven Commerce Optimization Platform
A suite of agents for a retail brand covering ad performance monitoring, content scheduling, competitive price tracking, and inventory alerts — giving the team real-time intelligence without a team of analysts behind it.
FAQ
Common questions
- How do we know the AI will give accurate answers and not hallucinate?
- This is the right question to ask, and it shapes how we architect every AI system. The most important safeguard is grounding the model in your actual data through retrieval-augmented generation, so it answers from your documents rather than inventing from training. Beyond that, we build evaluation frameworks that test the system against known questions, logging that lets you audit what it said, and human review steps for high-stakes outputs. Hallucination is a real risk with LLMs — the engineering is in managing it, not pretending it doesn't exist.
- We are not a tech company. Is agentic AI realistic for us?
- Yes, and it's often the non-tech businesses that see the clearest ROI. The use cases aren't exotic: an assistant that can look up your procedures and answer staff questions, a workflow that processes a document type you handle every day, an agent that monitors something you currently track manually. We scope these projects to match your team's comfort level and build the human oversight model around how your people work. You don't need to understand how the model works — you need to trust the output, and we build so you can.
- How quickly does this space change, and will what you build still be relevant in two years?
- The underlying capability is improving rapidly — models get better, prices drop, new tools appear. That's actually an advantage if you build the architecture correctly. We design systems to be model-agnostic and modular, so you can swap in a better model or add a new tool without rebuilding from scratch. The business logic, the integrations, and the data grounding you build today are durable; the specific model underneath is a parameter you can update. We plan for the landscape to keep moving, and we build so that movement works in your favour.
Have a project for us?
Let's build something that works — across the whole stack.
Tell us what you're building — we'll get back to you fast.