/ service · 02
AI & Agentic Systems
AI that runs in production has an owner, a cost ceiling, and a failure mode. We design all three before we design the prompt.
/ service · 02 — what we ship
What this covers
We build AI systems that sit inside a product, not beside it. That means retrieval that returns evidence rather than vibes, evaluations that tell you when the model got worse, and guardrails that fail closed instead of failing confidently.
Retrieval pipelines (RAG)
chunking, hybrid search, reranking, citation enforcement
Agent orchestration
tool use, planning loops, specialist handoff
Trading signal generation
feature computation, backtest harness, live/paper parity
Structured extraction
turning messy documents into typed, queryable records
Workflow automation
approvals, reconciliation, and ops tasks with a judgment step
Evaluation harnesses
regression suites that run on every model or prompt change
/ service · 02 — how we build it
How we scope AI work
This is the part most providers skip, so we write it plainly.
Before we build anything
Every AI engagement starts with an evaluation set, not a model choice. We assemble 50–200 representative examples from your real data — the messy ones, the ambiguous ones, and the ones crafted to break us. If the team cannot agree on the right answer to a case, a model will not resolve it for you.
On cost
LLM spend compounds quietly and nobody notices until the invoice. We measure tokens per operation, set a hard ceiling, and design retrieval so the model reads four hundred words instead of forty thousand. Caching, model routing, and batching move the number more than any prompt trick.
On failure
Models fail quietly — a confidently wrong answer is worse than no answer at all. So we design for abstention: the system is allowed to say it is not confident and route to a human. We would rather ship something that knows its limits than something that sounds certain about everything.
/ service · 03 — what you get
What you get on delivery
- An evaluation suite that runs in CI and fails the build on regression
- Retrieval configuration with citation enforcement
- A token and cost budget with alerting, not a hopeful estimate
- Documented abstention and escalation paths
- Monitoring for drift, latency, and quality over time
- A written record of what the system is allowed to do unattended
/ service · 04 — the honest part
What we will tell you honestly
If a workflow does not need a language model, we will tell you. Plenty of "AI" problems are a queue, a cron job, and a lookup table — and those are cheaper, faster, and more predictable than anything we could prompt. We would rather hand you the boring solution that works for ten years.
/ where this shows up
Fintech & tokenized assets
RWA platforms, regulated token launches, KYC-gated flows.
DeFi & marketplaces
DEX integrations, presales, vesting, on-chain order books.
Crypto trading & bots
Signal engines, criteria-based execution bots, market monitors.
AI automation & tooling
LLM workflows with real tool access, compliance-aware platforms.
Got something in this area?
Tell us what you are working on. We reply within one business day with whether it is a fit and what the next step looks like.