AI consulting from people who ship AI, not slide decks

Too much AI consulting has a product problem: the product is the deck. A strategist interviews your team for two weeks, delivers sixty slides about "transformation," and is gone before anyone has to make a single thing work. Six months later the slides are in a drawer and the only AI in the building is whatever your staff quietly paste into a free chatbot.

We come at this from the other side. We're an engineering shop that ships AI features — retrieval-backed assistants, lead-qualification flows, content pipelines — inside the same Next.js stack we build our custom websites and webapps on. When we advise on AI, the advice is shaped by what survives contact with production: latency, hallucination rates, token costs at real volume, and the unglamorous state of most companies' data.

What AI consulting means in our hands

The audit starts with your workflows, not the technology. We map where hours actually go and where errors actually happen — intake, quoting, support, reporting — and only then ask which of those a model can genuinely absorb. Use cases get ranked by payback and risk, not by demo appeal.

"You don't need AI for this" is a real deliverable. A meaningful share of what gets pitched as AI is a cron job, a validation rule, or a SaaS setting nobody turned on. When the honest answer is boring automation, the roadmap says so, with the cost difference in writing. We'd rather lose a build than sell you one you don't need.

Model-agnostic. The build-vs-buy analysis compares like with like: what the vendor charges at your projected volume, what a build costs to run and maintain, and where the crossover sits. If one of our own Redenn Systems is a candidate, it is priced in the same table as the alternatives.

How we evaluate models and vendors

We're model-agnostic across Claude, GPT, Gemini, and open-weight options. Four criteria, in order: accuracy on your task (measured with a small eval set built from your real examples, not vendor benchmarks), latency, cost at production volume, and privacy / data-residency fit. For prototypes we reach for the Vercel AI SDK, retrieval on Postgres + pgvector, and evals before demos — if a recommendation can't survive a fifty-example eval, it doesn't go in the roadmap. And if the roadmap ends in a build, we can be the ones who build it — or hand the spec to your team.

What this looks like in practice

Two typical engagement shapes:

The opportunity audit. A service business drowning in repetitive inbox, intake, or quoting work wants to know what AI can actually take off its plate. We interview the team, map the workflows, and deliver a ranked roadmap. Sometimes the top item is an LLM-backed intake assistant; sometimes it's plain automation with no model in it. Either way you get the reasoning and the numbers, and a plan you can execute with us or with anyone else.

The feasibility spike. A company already sold on a specific idea — or being pitched hard by a vendor — wants proof before committing. We build a thin working prototype against a sample of your real data, run an eval, model the cost at production volume, and deliver a build-vs-buy memo. It's the cheap way to avoid an expensive mistake in either direction.

Pricing

AI consulting is scoped to the project. An opportunity audit for a five-person firm and a feasibility spike for a two-hundred-seat rollout are different animals, and pretending one price list covers both is how slide-deck consulting happens. Tell us what you're weighing and we'll come back with a fixed-price scope — contact us for a quote.