The technical partner agencies call when the white-label platform can't do it. We architect and build custom AI under your brand — you sell the work, own the client, and invoice at your own rate. Your client never knows we exist.
No pitch deck. Bring a request a client has actually made — you'll leave knowing whether it's buildable, roughly what it takes, and what to quote.
They've seen a competitor running AI chat, heard about AI receptionists answering calls, want automation in their lead follow-up. They expect their agency to handle it.
It's the right question, and a verbal assurance isn't an answer. Here is the contractual structure — this is what makes an arrangement white-label rather than plain subcontracting.
Signed before scoping. Who your client is never leaves the engagement.
Contractually prohibited. Nothing we hand over carries our name, in code comments, documentation, or UI.
Work-for-hire. Code, data, and infrastructure become the agency's outright — no licensing-back, no retained rights.
We do not contact, market to, or work directly with your client — during the engagement or after it.
We join only if you invite us, and when we do it's under your agency's name and email address.
We keep the right to publish an outcome with no identities attached — "a 40-person SEO agency's client, quote turnaround X → Y". Pattern and result, never who.
Most white-label AI providers are platform resellers rebranding the same chatbot, or offshore shops supplying bodies. Both are genuinely fine for commodity requests — if your client wants a standard site chatbot, use a platform and keep the margin. Call us for the work underneath that.
Pipelines where several agents each own one job, with clean handoffs and a human approval gate — not one prompt asked to do everything.
Workflows wired into a client's actual systems — CRM, ERP, catalogs, quote history — not a demo running on sample data.
Scoring, ranking, and prediction models trained on the client's own history, with the evaluation structure to prove they work.
When a client wants to productize rather than automate — a working product, phased so there's something testable at every stage.
Built to survive real failure: retries, audit trails, monitoring, and a defined answer for what happens at 3am when something breaks.
A prototype that impressed everyone and won't survive production. We take those from demo to deployed.
Every number below is from a published case study on this site — click through and check them.
A four-stage pipeline for a B2B procurement team: parse the request, match suppliers, price in context, rank for approval. RFQ research went from 30+ minutes to under 60 seconds, team capacity effectively tripled with no new headcount, and recommendation accuracy passed 90% after the second build iteration.
An interior design platform delivered across five phases and multiple build cycles, combining image and text input with semantic search that consistently outperformed keyword search in user testing. The client had a working, testable product at every stage — not just at the end.
Three channels running simultaneously on one automated pipeline, 24/7, with per-channel editorial identity preserved. Production went from hours per video to minutes of human oversight at setup. Adding a fourth channel is configuration, not engineering.
Behind the work: M.S. Computer Science, 10+ years applied AI, and 10+ peer-reviewed publications. You get the senior person on the build, not a project manager relaying to a team you never meet. Meet the team →
You bring what's being asked for. We tell you what's buildable, what isn't, and what we'd push back on. Straight answers, including "don't sell this yet" when that's the honest one.
You get a written scope and a fixed price to mark up however you choose. No hourly surprises landing mid-project while you're exposed to your client.
Under your brand, on your timeline, with progress you can relay to the client in their language rather than ours.
Documented handoff to your team, or we stay on for maintenance and iteration. Your call, and it can change later.
Project work starts at $15,000 and scales with integration complexity, data readiness, and how many systems have to be touched. You mark up at whatever rate your market supports.
If AI work is becoming a recurring line in your delivery rather than an occasional one-off, a monthly retainer for senior build capacity is the better structure. It's more predictable for both sides than pricing every project cold, it means you can quote confidently before the scoping call, and it keeps capacity reserved when your pipeline is full.
We work with agencies in the US, UK, and Australia — async-first, with scheduled overlap for scoping and review.
Tell us what your client is asking for. We'll reply within one business day.