Generative AI that runs on your knowledge
Large language models are remarkable at general knowledge and unreliable about your business. We build generative AI applications grounded in your documents, your data, and your rules, for content, knowledge management, document processing, search, and the daily work that eats your team's hours.
The model knows everything except your business
Grounding is the difference between a chatbot that guesses and an application that answers from your actual policies, prices, and history.
Book a strategy session ($350)A good fit if you
- Have knowledge locked in documents, wikis, and people's heads that nobody can find fast
- Produce content, proposals, or reports that follow a pattern and eat senior time
- Answer the same questions from staff or customers repeatedly
- Process documents where the answer is inside but hard to reach
- Have tried public AI tools and hit the wall of hallucination and generic output
Generative AI is the class of models that produce language, code, and images: writing, summarizing, translating, answering, drafting. On its own, a model like this knows the public internet and nothing about you. The applications we build fix that. They retrieve the relevant parts of your own material, hand it to the model as context, and constrain the output to what your business can stand behind.
That technique, retrieval-augmented generation, is the foundation of most of what's useful about generative AI in a business. A knowledge assistant that answers from your handbook and your contracts. A proposal drafter that pulls from your past proposals and current pricing. A document processor that reads what came in and produces what needs to go out. A content system that writes in your voice about your services for your markets, in three languages.
We're careful about where generative AI belongs. It's excellent at drafting, summarizing, and finding. It's dangerous when its output is trusted without a check on decisions that matter. Every application we build has a review step where one is warranted, logging on everything, and clear signals when the model isn't confident.
What we build with generative AI
Applications, not chat windows. Each one is connected to your data and embedded in your workflow.
Tools and stack we work in
- OpenAI
- Anthropic
- Google Gemini
- Llama
- LangChain
- LlamaIndex
- Pinecone
- pgvector
- Weaviate
- Supabase
- Azure OpenAI
- AWS Bedrock
Model choice follows the job and your data policy. We use hosted frontier models where they're the right tool and open models inside your environment where the data can't leave. Often an application uses more than one.
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Knowledge management assistants
Ask a question, get an answer from your own documents, policies, and history, with the source cited so it can be checked.
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Document processing
Intake documents read, understood, and turned into the output you need: a summary, a structured record, a response, a next step.
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Content generation systems
Service pages, product descriptions, proposals, emails, and social content produced in your voice, grounded in your facts, in English, Spanish, and Portuguese.
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Internal search
Search across your files, tickets, and systems by meaning rather than keyword, so the answer surfaces even when nobody remembers the file name.
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Drafting and productivity tools
First drafts of reports, quotes, responses, and summaries produced for a person to review and send, cutting the blank-page time.
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Customer-facing assistants
Grounded assistants that answer product, policy, and status questions from your real data, and hand off to a person when they should.
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Translation and localization
Content adapted, not just translated, for Spanish- and Portuguese-speaking markets, with your terminology preserved.
Where generative AI earns its place
The pattern is the same across industries: knowledge that's hard to reach, and writing that follows a template but takes senior time.
Insurance agencies
Coverage questions answered from carrier documents and your own policies, application summaries for underwriting review, and renewal communications drafted per client.
Legal and professional services
Research and drafting grounded in your matter files and templates, client update letters, and internal knowledge search across precedents.
Medical and aesthetic practices
Patient education content in the practice's voice, pre-visit instructions personalized by treatment, and internal protocol assistants for staff.
Home services and trades
Estimate narratives from job notes and photos, service descriptions and FAQs for every trade and city you serve, and technician assistants that answer from your manuals.
Real estate
Listing descriptions from property data, neighborhood guides for every area you cover, and buyer and seller communications drafted per stage.
E-commerce
Product descriptions and category content at catalog scale, support responses grounded in order data, and merchandising copy for campaigns.
Why a marketing agency builds this
We were early users of generative AI because content is our job. We learned quickly what it's good at, where it lies, and how to ground it so it stops. We also learned that the same technique that makes a content system trustworthy, retrieval from your own material with a human check, is what makes a knowledge assistant or a document processor trustworthy. So we build both.
The connection to growth is direct. A service business with a grounded content system can publish a page for every service in every city it serves, in every language its customers speak, and keep them accurate. That's what ranks in search and gets recommended by AI. We build the system, and we run the SEO and AI search strategy it feeds.
How we work
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Discovery and data audit
We learn how the business makes money, where the decision or the workflow actually breaks, and what data exists to fix it. If the data isn't there yet, that becomes step one.
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Roadmap and architecture
One plan that names the outcome, the integrations, the guardrails, and the budget. Sequenced so the fastest win funds the longer build.
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Build and integrate
We build inside your systems and your accounts, connect to the tools you already run, and test against real data before anything touches a customer.
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Measure and improve
Monthly reviews on the business number the system was built to move, not on model accuracy in isolation. We keep tuning after launch.
The same standards as everything else we do
You own the code and the data
Source, models, prompts, pipelines, and accounts are yours. If we ever part ways, everything we built stays with you.
Revenue model first
We ask how the company makes money before we ask which model to use. AI that doesn't move a business number is a science project. It's how we work on everything.
No hidden fees
Scope is written before it's priced. Cloud and API costs are passed through at cost, not marked up.
Senior people, always
The engineer who scopes your system is the one who builds it. No handoff to a junior pod after the contract.
Guardrails by default
Human review where decisions carry risk, logging on every automated action, and a kill switch you control.
Three languages
Systems that read, write, and talk in English, Spanish, and Portuguese, because your customers do. See where we operate.
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Questions we get asked
It's the technique of finding the relevant parts of your own documents and giving them to the model as context before it answers. It's the difference between a model guessing from general knowledge and an application answering from your actual policies, prices, and history, with sources it can cite.
Ungrounded models do. Grounded applications with retrieval, constrained output, and confidence signals rarely do, and when they're uncertain they say so or route to a person. We test against your real questions before launch and measure accuracy in plain terms.
No. We use providers with contractual no-training guarantees or run models inside your environment. Your documents and conversations stay yours.
Yes. We build style and terminology guidance from your existing material, ground every piece in your facts, and produce drafts for review. The output reads like you, not like a generic model.
Natively. Content is produced for the market, not translated from English, with the register and vocabulary of the specific country where that matters.
We begin with one high-value use case, usually a knowledge assistant or a document workflow, and ship it in four to eight weeks. Once it's proving itself, we extend to the next one on the same foundation.
Your knowledge is an asset. Make it answer.
Bring the documents your team searches most and the writing that eats the most senior hours. Thirty minutes tells us where generative AI fits first.
Book a strategy session ($350)
