LLMs · agents · RAG · n8n
AI features built with senior engineering judgment
We build AI features into your existing application: LLM integrations, retrieval-augmented generation on your data, agents that handle multi-step work, and workflow automation with self-hosted n8n. Senior engineers use AI tools (OpenAI, Anthropic, Cursor) as a force multiplier, not a shortcut. Code gets reviewed. Output gets tested. Nothing ships that we wouldn't run ourselves.
Talk through what you want to buildWho this is for
Two kinds of buyers, one standard for what ships
AI features we build have to clear a bar: code your engineers can maintain, costs you can predict, and AI features that hold up under real usage instead of just in the demo. Most clients evaluating AI work fall into one of two camps:
Founders and product leaders
You want AI features in your product. You don't want a science project.
You have a working application with users. You see places where an LLM, a retrieval system, or an agent could make a difference: answering customer questions from your docs, drafting content, classifying inputs, automating a multi-step flow. You don't want a flashy demo that falls apart in production. You want features that work, that don't surprise you with a $30,000 API bill at the end of the month, and that your engineers can read and extend.
Operations and business leaders
You have a manual process that should be automated. AI might be the answer.
Your team is doing work that looks like a good candidate for an agent: reviewing documents, routing requests, extracting data, orchestrating tasks across systems. You're not sure what's actually feasible, what's hype, and what would create more problems than it solves. You want someone who'll evaluate the work honestly and tell you when AI isn't the right tool, not just sell you whatever's in season.
How it works
Decide. Prove. Harden.
Decide if AI is the right tool
We start by understanding the actual problem. Sometimes a Postgres full-text search, a rules engine, or a better workflow gets the result for a fraction of the cost and risk. If AI is the right answer, we'll explain why. If it isn't, we'll say so before scoping a build.
Ship the smallest version that works
We build a focused first version, usually two to six weeks, using established LLM APIs (OpenAI, Anthropic, or others your data sensitivity calls for). The goal is behavior in front of real users early, with evaluation in place so 'it works' means something measurable.
Harden for production
Cost instrumentation, caching, model fallback, hallucination guards, monitoring, and human-in-the-loop where the stakes call for it. AI features that pass review, hold up under real volume, and don't introduce the kind of fragility that bites you six months in.
What's covered
What we build, and how we keep it sustainable
LLM integration and RAG
OpenAI, Anthropic, Gemini, and others wired into your existing application, behind an abstraction layer so swapping providers is a config change. When answers need to come from your own documents and data, retrieval-augmented generation with vector search and embeddings tuned for the actual content.
AI agents and agentic workflows
Multi-step reasoning with tool use, including MCP-based integrations. Built with explicit guardrails, observability, and the human-in-the-loop checkpoints that keep agents from going off the rails.
Data extraction pipelines
Turning unstructured content (PDFs, transcripts, scans, emails) into structured data your application can use. Validation at each stage so you trust what gets written to the database.
Workflow automation with n8n
Self-hosted workflow automation that ties together your apps, AI services, and internal tools. AI-native nodes built alongside custom code, with the observability and version control your engineers already expect. Migrations from Zapier or Make when costs or data sensitivity outgrow SaaS.
Cost and infrastructure optimization
Token discipline, caching, batching, and right-sizing the model for the task. Self-hosted vs. API analysis when data sensitivity or volume justifies it. Bills that match the value delivered.
Evaluation and guardrails
Test sets that catch regressions, hallucination guards where accuracy matters, content filtering, and observability that tells you when an AI feature drifts before users notice.
In practice
What responsible AI engineering actually looks like
Aviation maintenance — structured procedures from PDFs
An aviation maintenance platform needed structured, step-by-step procedures for mechanics to follow on each job — every task from an oil change to a full inspection. The data didn't exist anywhere in usable form. It only lived in OEM service manuals: hundreds of pages of dense PDFs per aircraft type. Rebuilding by hand would have meant months of domain-expert time the client couldn't spare. Innovise engineers built a pipeline that ingested the manuals, used LLMs to extract procedures, normalized them into discrete steps with required parts and tools, validated the output, and wrote the structured result into the application's database. The MVP build extracted over 1,200 procedure steps across dozens of service offerings — five hours of engineer time, under ten dollars in API spend. A production-grade version would add heavier validation, edge-case handling, and the operational tooling around it. Even with that work added, the math against months of manual domain-expert extraction isn't close. The web app shipped on day one with usable procedure data, and the same pipeline ingests new manuals as they arrive.
Search feature — when AI wasn't the right tool
A client's in-house team was convinced that adding search to their application required a backend rewrite, a vector database, and an LLM in the loop. Innovise was brought in to assess. The actual problem was a search problem, not a semantic one. The solution turned out to be a lightweight write pipeline that synced a subset of fields to an Algolia index on each record update. No migrations. No model. The search feature shipped in a fraction of the time and budget the team had estimated. AI is a powerful tool. It's not always the right one.
How we deliver — senior engineers, AI tools, code review
Tools like Cursor, paired with experienced engineers, produce code that holds up under review at speeds that weren't possible two years ago. Paired with inexperienced engineers, they produce code nobody can maintain. Innovise uses AI tooling on every engagement as a force multiplier: code generation, test scaffolding, refactoring at speed. Every line goes through review and testing the same as hand-written code. The result is faster delivery without the slop that's making other AI-built codebases unworkable a year in.
What's different
We'll tell you when AI isn't the answer
Some firms selling AI work won't tell you when AI isn't the right tool. The incentives don't favor it. A traditional refactor, a rules engine, or a better-designed query is harder to sell than an agent. The result is a market full of AI features that didn't need to be AI features, and that the client can't afford to keep running.
Innovise's approach is the different. We'll chat about whether AI is genuinely the right tool for what you're trying to do. If a deterministic solution gets the result with less risk and less ongoing cost, that's what we'll recommend. When AI is the right answer, the engineering bar is the same as every other piece of code we ship: reviewed, tested, observable, and maintainable by your team after we're done.
Based in Bellevue, WA and working with clients nationwide. All work is done on-shore: faster turnaround, better context, no timezone gaps between you and the engineer doing the work.
Common questions
Frequently asked questions
What if my use case needs near-perfect accuracy?
We'll tell you that upfront. LLMs are good at drafting, classifying, summarizing, and extracting structured data with human review in the loop. They're bad at math, deterministic logic, and anything where 'mostly right' is dangerous. The first conversation includes whether your use case is in the right zone for AI at all. If it isn't, we'll say so before you write a check.
How do you keep API costs from running away?
By treating tokens like infrastructure spend, not a black box. That means choosing the right model for each task instead of defaulting to the most expensive one, caching aggressively where the same prompts repeat, batching where it makes sense, and instrumenting usage from day one so you see costs accumulate before they surprise you. We've cut a client's Azure bill by 80% applying the same discipline to traditional infrastructure. The same thinking applies here.
Are we locked into one AI provider?
Not by default. We architect AI features behind an abstraction layer so swapping from OpenAI to Anthropic, or to a self-hosted model, is a config change rather than a rewrite. Some clients want this for cost flexibility. Others want it for risk management. Either way, the option stays open.
What about data privacy and security?
Depends on the data and the use case. Some clients are fine sending data to a major provider's API under enterprise terms. Others need self-hosted models for regulatory or competitive reasons. We'll walk through the actual sensitivity of your data, the providers' data-handling commitments, and whether self-hosting is worth the cost. The default isn't always the right answer.
Do Innovise engineers use AI coding tools themselves?
Yes, and we're particular about how. Tools like Cursor combined with MCPs are amazingly powerful. They become a force multiplier when used by experienced engineers. The code ships faster, but the review bar stays the same. When AI-generated code isn't applied carefully, nobody can maintain it. Every line that ships goes through review and testing the same as hand-written code. We're happy to share what works (and what doesn't) with your team.
Is it ever the wrong call to add AI?
Often. We've talked clients out of AI features when a Postgres full-text search would have done the job, when a deterministic rules engine would have been more reliable, and when the underlying business problem wasn't a software problem at all. The honest answer is the most useful one. If the right tool isn't AI, we'll tell you.
Why n8n instead of Zapier or Make?
Depends on the situation. Zapier and Make are excellent for off-the-shelf SaaS connections at small to medium volume. They're fast to set up and don't require infrastructure of your own. They also get expensive at scale, and they don't keep your AI calls or your data on infrastructure you control. n8n is open source and self-hostable: when a workflow runs thousands of executions a day, when sensitive data shouldn't leave your network, or when you need custom code beside the visual nodes, the migration usually pays for itself. We'll help you assess the threshold and run the migration if it's the right call. If your current SaaS tools are fine for what you need, we'll say so.
How long does an AI feature build typically take?
Most first-version AI features ship in two to six weeks. A well-scoped LLM integration with caching and basic evaluation is on the shorter end. A retrieval-augmented system over a real document corpus, or an n8n workflow that orchestrates several systems with AI in the loop, is on the longer end. We don't quote a hard timeline until we've seen what you have and what you're trying to do.
Other services
AI hype is everywhere. Working AI features are rarer.
Bad AI features ship fast and become expensive to live with. Good ones take a few extra weeks of judgment up front. Start with a conversation: tell us the problem and what you're hoping AI will solve. We'll be honest about whether it's the right tool, what it would take, and what it'll cost to run in production.
Talk through what you want to build