Agents that act, with guardrails
Multi-step orchestration, tool use, permissioned actions and human checkpoints — built to run unattended and be audited afterwards.
orchestration graphs · tool sandboxes · eval harness · trace & replay
AI-native engineering studio
AI-nativeengineering
AI-nativeengineering
We build the intelligent systems most agencies can't. Agents, applied ML, web3 protocols and financial infrastructure — all outputs of one AI capability, engineered and operated by one partner.
4.9 · Clutch [slot]120+ systems shipped18 years in production engineering
NORTHWINDAXIOMMERIDIANVERTEXHALCYONOBSIDIANNORTHWINDAXIOMMERIDIANVERTEXHALCYONOBSIDIAN
01Capabilities
One capability.
Every kind of system.
We don't run separate practices. We run one — applied intelligence — and it shows up across every kind of system we build.
Models tuned to your domain
Retrieval, extraction, classification and fine-tuning on proprietary corpora — measured against a benchmark you can defend.
RAG pipelines · eval sets · fine-tunes · drift monitoring
On-chain, audited by design
Contracts, indexers and wallet-grade infrastructure with deterministic test suites.
solidity · indexers · MPC key flows
Low-latency plumbing
Market-data ingestion, execution pipes and risk tooling. Engineering only — never advice.
tick pipelines · p99 budgets · replay harness
The product around the model
Interfaces, billing, permissions and the platform that keeps intelligence shippable.
design systems · infra as code · SLOs
Interfaces people trust
Product design, design systems and prototyping — so the intelligence underneath is usable, and the product looks the part.
UX research · design systems · prototyping · motion
Labs
Meet EllaWe fund our own research, then productise it
Agentic prototypes, permission models and evaluation methods. What survives Labs becomes client-grade.
02Selected work — demo data
Case 01 · Agentic AI
A claims desk that closes its own loop
A permissioned agent reads intake documents, drafts adjudication, and escalates anything below a confidence floor. Every action is traced and replayable.
Architecture
Graph + sandbox
P95 latency
1.4s / step
Eval coverage
312 cases
Case 02 · Financial infrastructure
Market-data plumbing that stops guessing
Normalised multi-venue ingestion with deterministic replay, so teams can reproduce any minute of the day. Infrastructure only — no strategy, no advice, no return claims.
Throughput
1.2M msg/s
P99 ingest
840µs
Replay
Bit-exact 90d
Case 03 · Applied ML / NLP
A custom pipeline for a 40-year archive
OCR repair, entity resolution and a domain fine-tune turned an unsearchable corpus into a retrieval layer with a defensible accuracy benchmark.
Pipeline
7 stages
Retrieval F1
0.91 / 0.63
Corpus
2.1M docs
Case 04 · Web3
Settlement rails with deterministic tests
Contracts and an indexer with a full replay suite, so every state transition is reproducible before it ever reaches mainnet.
Coverage
100% branch
Audit findings
0 critical
Indexer lag
< 1 block
03Labs · prototype
Ella acts for you — only where you allow it
A permission-based agent that carries out real tasks on your behalf. It asks before it acts, works inside the scopes you grant, and keeps a reversible ledger of everything it did. Applied agentic AI — custom orchestration and permissioned on-device actions, not a black box.
You grant scopesIt asks before actingEvery action loggedReversible for 30 days
04Proof — placeholders, swap for real marks
Teams that ship
with us.
4.9 Clutch · [slot] Top AI Developer · 2026 [slot] DesignRush · Verified [slot] ISO 27001 · [slot]
“They shipped the orchestration layer our last two vendors said couldn't be made reliable.”
VP Engineering, [Company]
“One team took us from research notebook to a monitored production pipeline.”
Head of Data, [Company]
“Latency budgets were treated as contracts, not aspirations.”
CTO, [Company]
05Before the first call
Questions
we're asked most
What does “AI-native engineering” actually mean?
Architecture decisions start from the model: what it does reliably, what it doesn't, and what evaluation proves it. The product, data and infrastructure are engineered around that answer.
Do you build financial products or give financial advice?
No. We build infrastructure — data pipelines, execution plumbing, risk tooling and monitoring. We do not provide investment advice, manage assets, or make performance claims.
How do engagements start?
A short paid discovery: we map the problem, define an evaluation, and return a plan with a fixed first milestone. You keep everything produced, whether or not we continue.
Can you work with our existing team?
Yes — embedded alongside your engineers, or as a dedicated pod that owns a surface end to end. Either way you get the traces, evals and docs, not just the code.
06Start here
Let's build
the impossible.
Tell us the system you can't get built. We come back with a short, paid discovery — a clear plan and a fixed first milestone — usually within two working days.
01
Discovery
A working session to map the problem and define what "good" is measured against.
02
The plan
Architecture, milestones and a fixed first deliverable — yours to keep, either way.
03
We build
Embedded with your team or as a dedicated pod, shipping with traces, evals and docs.