We build AI product features and automation using LangChain, LangGraph, retrieval-augmented generation, and workflow platforms like n8n, so the result is a working part of your product, not a demo.
Discuss an AI ProjectAdd intelligent search, summaries, assistants, recommendations, and workflow support inside an existing product.
Help teams access internal information through secure, searchable business knowledge systems.
Extract, organize, review, and process information from invoices, forms, PDFs, and business records.
Connect AI with APIs, databases, communication tools, and operational systems.
Automate scripts, voice, captions, visual selection, content preparation, and publishing workflows.
Build a focused first version of a new AI-powered product.
Define the task
Review data and systems
Select the right model and architecture
Build with review and fallback paths
Measure quality, cost, and usefulness
We build assistants that answer questions using your own documents, policies, and product data through retrieval-augmented generation. Instead of relying on a model general knowledge, the assistant retrieves the right internal content first and then generates an accurate, grounded answer, so staff and customers get correct information drawn from your actual systems.
Invoices, forms, contracts, and reports can be read, classified, and turned into structured data automatically. We design these pipelines to include validation and human review steps where accuracy matters, so automation reduces manual work without removing oversight where it counts.
For tasks that involve several steps, such as reading a document, checking it against a policy, and taking an action, we use LangChain and LangGraph to let a model plan and execute a sequence of steps instead of just answering a single question. This is what turns an AI feature into real automation rather than a chatbot bolted onto a product.
Many automation needs do not require a language model at all, just a reliable way to connect systems. We use n8n and direct API integrations to route data between your product, your CRM, payment providers, and communication tools, bringing in an AI model only for the steps that genuinely need judgment or language understanding.
Every AI feature we ship includes human review where it is needed, clear data access controls, cost monitoring, error handling, and logging so behavior can be evaluated over time. We are direct about where a model is reliable and where it is not, and we design fallback behavior for the cases it should not handle alone.
Yes. We start by mapping the specific workflow the AI feature needs to support, then ship it as an incremental addition to the product you already operate — no rebuild required. A typical first AI feature (e.g. automated summarization, a recommendation step, or a support-triage flow) is scoped, built, QA'd by automated agents, and human-reviewed before it goes live, following the same Discover → Shape → Design → Build → Launch → Improve process used for every Briskbase engagement.
We don't lock every feature to one provider. The model is chosen per task — weighing cost per request, accuracy on that specific job, latency, and whether the data can leave your infrastructure — so a high-volume, low-risk task might run on a smaller, cheaper model while a customer-facing or high-accuracy task runs on a stronger one. This gets revisited as models and pricing change, rather than fixed once at project start.
Yes. Private business data is scoped with explicit access controls before any AI workflow touches it — defining exactly which data sources a given feature can read, what it can write back, and where human approval sits in the loop for anything sensitive or high-risk. This is agreed during the Discover/Shape stages, before build starts, not bolted on afterward.
Three levers: picking the right-sized model per task instead of defaulting to the most expensive one, monitoring actual usage so cost is visible rather than discovered on an invoice, and architecture choices that cache, batch, or short-circuit AI calls where a simpler rule-based step would do the same job for less.
Yes. For anything customer-facing, financial, or otherwise high-stakes, a human approval step sits in the workflow before an AI-generated action takes effect — the AI drafts or recommends, a person confirms. Lower-risk, high-volume tasks can run fully automatically once accuracy has been validated in practice.
Yes. An AI MVP is scoped down to one clear use case — one workflow, one measurable outcome — and delivered as a standalone build so it can be validated with real users before deciding whether to expand it, following the same weeks-not-months delivery approach used across Briskbase products.
Pricing is scoped per engagement — contact us for a quote.
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