◆ AI
Artificial Intelligence
AI features that survive real users — not just a demo.
We design and ship AI-native products: retrieval, agents, evaluations, and the production guardrails around them. The goal is software that stays accurate, affordable, and reliable once real traffic hits it.
Scryptflo
AI
A working slice on your real data in days
Releases gated on an evaluation suite
A token budget agreed up front
You own every prompt and line of code
◆ Why it matters
What it costs to get this wrong.
The difference between software that demos well and software that holds up is everything you don't see in a screenshot. Here's where we spend it.
A polished demo gets a yes in the room, then falls over the first week real users touch it.
Built for production, not the pitch
Evals, fallbacks, and graceful degradation from day one — so it holds up long after the demo is over.
One confident, wrong answer and users quietly stop trusting the entire product.
Answers you can verify
Grounded retrieval, citations, and guardrails keep every response checkable — for your users and your brand.
Token spend creeps up unnoticed until the invoice makes someone panic.
Cost you can predict
Prompt versioning, caching, and live monitoring keep spend flat and visible, not a month-end surprise.
◆ Scope
What we build.
RAG over your docs, data & knowledge base
Multi-step agents with tool use & memory
Natural-language analytics & BI
Document and data extraction at scale
In-product copilots & assistants
◆ Process
Every step of the way.
A clear, repeatable path from a blank page to software running in production — you always know what's happening and what's next.
What you can count on
- A working slice on your real data in days
- Releases gated on an evaluation suite
- A token budget agreed up front
- You own every prompt and line of code
- 01
Scope & evals
We define what success means and build an evaluation set before writing features.
- 02
Prototype
A working slice on your real data within days, not months.
- 03
Harden
Guardrails, fallbacks, retrieval quality, and cost controls.
- 04
Ship & monitor
Observability, prompt versioning, and iteration in production.
◆ What you get
Deliverables.
Evaluation suite
Automated tests that catch quality regressions before users do.
RAG pipeline
Chunking, retrieval, and citations tuned to your data.
Agent orchestration
Tool use, memory, and guardrails that stay on task.
Cost & usage dashboards
Token spend and latency, visible in real time.
Monitoring & alerts
Know the moment quality or cost starts to drift.
◆ Before you ask
The questions worth asking.
The things that actually decide whether this works for you — answered straight, before the call.
How do you know the AI is good enough to ship?
We build an evaluation set on your real cases before writing features, and we gate releases on it. Quality stops being a gut feeling and becomes a number you can see move.
What will it cost to run at scale?
We agree a token budget up front and defend it with caching, prompt versioning, and live monitoring — so cost is a decision you make, not a surprise you discover.
How soon will we see something real?
A working slice running on your real data in days, not months. You evaluate the approach early, while it's still cheap to change direction.
Do we own what you build?
Completely. Every prompt, pipeline, and line of code is yours — no lock-in, no black boxes you can't maintain without us.
◆ AI
Let's build the version that lasts.
Tell us what you're trying to ship. We'll tell you, honestly, how we'd approach it and what it would take — no pitch theatre.