Generative AI development services that trace every answer to a source.
LLM-powered products your users trust — grounded in your data, engineered for accuracy and cost.
We build generative AI products end-to-end: retrieval pipelines that ground answers in your data, model selection and fine-tuning where it pays off, prompt and context engineering, cost and latency budgets, and the product surface users actually touch. Whether it's a new AI-native product or AI features inside an existing platform, we treat accuracy as an engineering discipline — with evals, not vibes.
Not a feature list. A Tuesday morning.
Here's what each of these actually looks like once it's running against your real workflows — not the pitch, the mechanism.
RAG systems grounded in your documents & data
Ask the product a pricing question and it answers from this quarter's actual rate card, with a citation link back to the source page — not a plausible-sounding guess with nothing behind it.
AI-native product development, zero to launch
What starts as a Figma concept for an AI feature becomes a working product in weeks, not a slide deck — built by the same team that owns it after launch, not handed off cold.
Model selection, fine-tuning & cost optimization
The model powering your product gets swapped when a cheaper or more accurate option clears your eval bar — a change your users never notice, and your finance team does.
Eval-driven quality: measured, not assumed
Every prompt change runs against a golden dataset before it ships. 'Feels better' isn't a release criterion here — a measured accuracy score against real cases is.
Shipped, not promised.
Generative Catalog Intelligence for a Marketplace
A generative enrichment pipeline turning inconsistent seller listings into structured, search-optimized product data — descriptions, attributes, and categorization at catalog scale.
FeedDrop — AI Social Content Creation Platform
An AI-powered social platform where users generate videos and memes from prompts, share them on a TikTok-style feed, and remix each other's work into viral chains — with portfolios and admin moderation built in.
ReUniting AI — Interactive AI Companions
A platform that turns recordings of loved ones into interactive AI companions: hyper-realistic video avatars and near-human voice cloning, delivered through real-time chat and voice — with consent and privacy engineered in from the start.
Not hypothetical. Shipped, by vertical.
Only the industries where this exact capability has a real, running engagement behind it — not a generic list of who we'd like to work with.
Concrete, not conceptual.
Every engagement under this capability produces the same kind of artifact — reviewed weekly, owned by you from day one.
See what generative ai would look like in your stack.
The path to production.
Discovery
Define users, the AI's job, quality bar, and data sources. Kill weak use cases early.
Prototype
A working slice on your data in 2–3 weeks — enough to validate quality with real users.
Engineer
Retrieval tuning, eval suites, guardrails, fallbacks, and cost controls to production standard.
Launch & learn
Instrumented rollout; accuracy and unit-economics reviews drive the roadmap.
How the stack orchestrates.
Chosen per engagement, never the other way around — this is how the pieces actually connect around the system we're building.
Asked on every first call.
It depends on your accuracy bar, data sensitivity, latency, and unit economics. We benchmark candidate models against your actual tasks during discovery and design for portability, so you're never locked to one vendor's pricing.
Grounding and measurement: retrieval pipelines that constrain answers to your data, citation requirements, confidence-based refusals, and evaluation suites that track factuality on every release. Hallucination is managed like any defect class — measured, budgeted, and driven down.
Yes — most of our generative AI work ships inside existing platforms. We integrate with your codebase, auth, and data layer, and design the AI surface to match your product's UX.
It depends on data complexity and how deep the integration runs — a validation prototype and a full production application aren't the same commitment, and we won't pretend they are. We also model your per-request inference costs upfront, so the economics work at scale, not just in the demo.
Let's put AI to work in your business.
A 30-minute call. You bring the workflow or the roadmap — we'll tell you what's feasible, what it costs, and what we'd build first.