LLMs, agents and retrieval — applied where they change a real number.
How we think about model choice, architecture and cost before recommending AI for a workflow.
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AI, Explained
Modern AI, in the context we build in, means large language models (LLMs) applied to specific tasks: answering questions grounded in your data, extracting structured information from unstructured text, classifying or routing content, and orchestrating multi-step workflows through agents.
The two building blocks that matter most in practice are retrieval-augmented generation (RAG) — grounding a model's answers in your actual documents at query time — and agents, which chain multiple model calls and tool uses together to complete a multi-step task.
Any workflow that involves reading, deciding, or writing at volume is a candidate: support tickets, contract review, research synthesis, lead qualification, internal documentation search.
The businesses getting genuine value aren't chasing the most advanced model — they're grounding a capable-enough model in their own data and wiring it into a real workflow with a way to measure and correct mistakes.
Architecture Possibilities
RAG architecture typically involves chunking and embedding your documents into a vector database, retrieving the most relevant chunks for a given query, and passing them to the model as context alongside the question — grounding the answer in your actual data rather than the model's general training.
Agent architectures add a decision loop: the model can choose to call tools (search a database, hit an API, run a calculation) and use the results to decide its next step, which is powerful for multi-step tasks but requires more careful guardrails since more can go wrong at each step.
Simpler tasks often don't need either — a well-structured prompt with defined output format is sometimes the entire architecture, and we default to that simplicity whenever it meets the accuracy bar.
Core Features
RAG Pipelines
Grounding model responses in your actual documents and data rather than general training knowledge.
AI Agents
Multi-step task orchestration with tool use, for workflows too complex for a single model call.
Evaluation Frameworks
Labeled test sets and accuracy tracking so performance is measured, not assumed.
Technology Stack
Where This Applies
- Support ticket triage and draft-response generation
- Contract and document review against a defined checklist
- Internal knowledge-base question answering
- Automated data extraction from unstructured documents
- Research synthesis from public and internal sources
- AI-assisted content and code review
Security, Scalability & Maintenance
Data handling depends on provider and architecture choice — we design around your specific data residency, retention and access requirements rather than defaulting to the least restrictive option.
Cost and latency scale with usage volume, which is why model choice (a smaller, cheaper model for high-volume simple tasks vs. a more capable model for complex reasoning) is a scalability decision, not just a quality one.
AI features are typically integrated into existing tools (a CRM, support platform, internal dashboard) rather than shipped as a standalone product, via API integration or embedded components. Model behavior, costs and the underlying data can all drift over time, so ongoing monitoring of accuracy and cost is part of operating an AI system responsibly, not a one-time setup.
Why This Is Worth the Investment
- Reduced time-per-task on high-volume workflows like support and document review
- Faster research and synthesis work across large volumes of unstructured information
- More consistent output quality than fully manual processes at scale
- A measurable, improvable system rather than an unmeasured manual process
Industries This Applies To
Frequently Asked Questions
What's the difference between RAG and fine-tuning?
Do we need a data science team to use AI in our business?
How much does it cost to run an AI feature in production?
Can AI hallucinate or make things up in our system?
Is open-source AI a viable alternative to commercial APIs?
Considering AI for your product?
We'll tell you honestly whether it fits before we recommend building it.