Navigating the frontier of
AI Agents
We research, build, and deploy production-grade AI systems, from multi-agent RAG pipelines to fine-tuned domain specialists, turning cutting-edge papers into working software.
Research-first, production-always
The field of AI moves at a pace that makes most development cycles look slow. A paper on a new retrieval technique, an improved embedding model, or a more effective agent reasoning strategy can shift what's achievable in a matter of weeks.
We stay current. We read preprints from arXiv, test techniques from DeepMind, Anthropic, Meta AI, and academic labs. Not for its own sake, but because each improvement translates directly into better outcomes for the systems we deploy. Our clients get the benefit of that research without needing to follow it themselves.
But research without execution is just speculation. Everything we explore gets evaluated against one question: does it make production software meaningfully better? If the answer is yes, it goes into our stack.
Towards fully automated intelligence
The end state we're working toward: AI systems that handle the repetitive, high-volume layers of customer interaction, so human teams can focus on the work that actually requires them.
E-Commerce Automation
Conversational AI that handles product discovery, order tracking, returns, and recommendations at scale, trained on a brand's own catalogue, tone, and policies. A customer asking a complex question at 2 AM gets the same quality response as one talking to your best support rep.
Customer Service at Depth
Beyond FAQ bots. We build agents that understand account history, escalate intelligently, query internal systems, and resolve multi-step requests without a human in the loop. The goal is resolution, not just response.
Domain-Specific Expertise
General-purpose LLMs know a little about everything. We fine-tune and align models to know a great deal about your specific domain: legal language, product catalogues, compliance requirements, internal processes. The result is a system that reasons like a subject matter expert, not a generalist.
From your data to a working AI, practically
RAG (Retrieval-Augmented Generation) sounds complex. The practical version is straightforward: the AI retrieves relevant pieces of your knowledge before generating an answer. Here's what that looks like in production.
You have an API
If your product data, knowledge base, or content is already served via a REST or GraphQL API, we connect directly. The AI queries your live data at inference time, meaning it always reflects the current state of your catalogue, pricing, or policies without any re-indexing.
You have documents
PDFs, Word docs, support transcripts, policy manuals, product spec sheets. We ingest, chunk, embed, and index them into a vector store. Chunking strategy matters enormously: we use semantic boundaries, sliding windows, and metadata tagging so the retriever finds the right passage, not just the right document.
Why chunking strategy matters
Chunking is where most RAG implementations fail silently. Split too aggressively and you lose context. The retrieved passage makes sense in isolation but misses the surrounding reasoning. Split too broadly and retrieval becomes imprecise. You get the right page but not the right paragraph.
We use a layered approach: semantic chunking at sentence boundaries, overlapping windows for context continuity, and per-chunk metadata (section title, document type, date) that can be used as a retrieval filter. A legal query filters to documents of a specific type. A product query filters to a specific category. The retriever finds not just semantically similar content, but the right kind of content.
Training AI into subject matter expertise
Retrieval can answer questions about your data. Fine-tuning shapes how the model reasons, responds, and represents your domain. The two approaches are complementary. Most production systems need both.
We work with LoRA (Low-Rank Adaptation) for efficient fine-tuning of large models without full parameter updates, making it practical to specialize a strong base model on domain-specific instruction data. For smaller models that need to run on-premise or with tight latency requirements, we handle full fine-tuning and quantization.
The result is a model that writes in your brand voice, knows your terminology, handles your edge cases, and refuses gracefully when a question falls outside its competence, rather than hallucinating a confident-sounding wrong answer.
Small team. Deep focus.
We're a small, focused team. Not a large agency taking every project that comes through the door. We work with a limited number of clients at a time so we can actually care about the outcome. If you have a real AI problem to solve, we'd like to hear about it.