Wrapper hallucinating in production? Competitor shipping the AI feature you've been scoping for six months? We engineer production AI with evals from day one, token economics that scale, and the right architecture for the real problem. New products or AI inside existing systems, we build for both.
AI productsshipped beyond demo
Evalsfrom day one
RAG, agentsand workflow AI
Token economicsbuilt into scope
AI inside existing systems
Cost and latency modeled before build
Observability before launch day
AI inside existing systems
Cost and latency modeled before build
Observability before launch day
The build spectrum
AI work fails when every problem becomes a chatbot. We map your use case to the right build mode before proposing architecture, cost, or timeline.
Search, retrieval, permissions, citations, and answer quality designed for company knowledge that changes every week. Useful for support, sales, operations, and policy-heavy teams.
92
Eval pass rate
14
Sources live
How AI engagements begin
Most AI work starts with urgency, confusion, or a demo that looked good once. These are the patterns we see first.
01
A chat UI is live, but answers drift, citations are missing, and nobody knows whether a prompt change improved the product or broke it. We add evals, retrieval checks, observability, and release discipline so the feature can earn trust again.
Typically ships
02
We map workflows, data readiness, expected value, and implementation risk. The output is not a brainstorm deck; it is a prioritized AI build plan with clear no-build calls.
Typically ships
03
Model choice, context size, caching, batching, routing, and human review design decide whether AI scales. We tune architecture around cost and latency before launch.
Typically ships
04
Agents need permissions, tool calls, audit trails, and fallback paths. We wire AI into CRMs, ERPs, support tools, and custom systems without letting it become an invisible risk.
Typically ships
Our process
Six stages from AI-fit decision to production iteration. Each stage creates artifacts you can inspect, approve, and keep.
We test whether AI belongs in the workflow at all, which build mode fits, and what value needs to be proven.
Sample artifact
APPROACH
COST
RISK
Visual mockup - real artifact replaces this in production
Sample artifact
APPROACH
COST
RISK
Visual mockup - real artifact replaces this in production
How we work
The same models we engineer for clients are the models we use to engineer for clients. If AI doesn't make our team faster, it probably won't make yours faster either. The cycle-time gains are how we ship the right architecture at a price most agencies still quote for a wrapper.
Codebase navigation, refactoring at scale, and feature implementation in conversation with the architect, not the IDE.
ADR drafts, design reviews, and second opinions on every non-obvious call. The senior engineer who's always available.
Evals running on every prompt change and every PR. The regression test for AI features.
Production observability for our own LLM usage, then yours. Token spend, latency, eval drift, visible by default.
First pass catches what tired humans miss. Second pass is the human, doing the judgment call.
Generated drafts, human-finalized. The artifact backlog never falls behind because nobody enjoys writing them.
These are our super weapons that help us build a strong structure for your organization!
Python
Next.js
React
NestJS
Django
Tailwind CSS
Java Script
FastAPI
Pricing tiers
Three starting points. Real number after a 20-minute scoping call. AI cost depends on data readiness, eval depth, integrations, and launch risk.
Audit
$12k
/ projectAI-fit, architecture, eval, and cost audit before committing to delivery.
Build
$45k
/ projectProduction AI feature, RAG workflow, or agentic workflow with evals and observability.
Custom
Talk
For enterprise AI platforms, regulated workflows, or AI deeply integrated with existing systems.
These are the words that keep us going even an extra mile!
The questions clients ask before putting AI into production.
Usually one of them, rarely all of them. We start with workflow, data, risk, and expected value, then choose the smallest architecture that can pass production quality gates.
For founders and operators scoping AI
A practical PDF for deciding what kind of AI build you actually need before the first call.
One email, the PDF attached. No drip sequence, no sales follow-up.
TODO - Genuine PDF resource to be authored before going live
PDF - 10 pages
Scope, spectrum, and cost - 2026
Three ways to work with reverseBits
Different buyers need different first steps. No push to a sales call before you're ready.
Soft - just exploring
A 10-page PDF on scoping an AI engagement - the five build modes, cost ranges, and an eight-question worksheet.
Download the briefMid - ready to explore
Tell us where AI might fit. Six-question qualifier first so we use the time well.
Book a scoping callNow - ready to start
Share your problem or existing system. Proposal back within one business day, including a named partner.
Send us your briefOther Build services
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