RedRobot
AI · Data · Blockchain
How we work
Whether the engagement is production AI, a data platform, or enterprise L1/L2, the control loop is the same: define success metrics, ship a first real version on your systems, measure against those metrics, harden, hand over. We optimize for architectures that survive design review, security review, and the first production incident, not for demo-day novelty.
01
Write accuracy, latency, cost, residency, and audit criteria. For chain work: signers, key custody, finality, and failure domains. If it is not written, it is not a requirement.
02
On your identity, data plane, and network, not a disposable sandbox. Scope is thin; contracts (APIs, schemas, ACLs) are real.
03
Groundedness, recall, cost per successful job, error budgets, or for chain, finality and custody SLOs. Kill or promote with evidence.
04
Observability, human gates on irreversible actions, runbooks, load and failure drills. Expand only the design that already clears the bar.
05
Credentials, docs, and operational walkthrough. Your team runs it. We return when the next scope is worth a new loop.
We integrate against your live identity, data stores, and when relevant, validators and signing infrastructure before locking architecture choices.
If accuracy, cost, residency, or (for chain) finality and key custody fail the written bar, we stop. Scaling a failed design is not delivery.
What is authoritative, who may read it, and what breaks if it is wrong, answered before model selection. Generative systems amplify errors in the source documents; they do not invent missing authority.
The same order for chain: shared state, identity, and key control before any market narrative.
Hybrid retrieval under ACL, private or VPC inference by default, human gates on side effects, and full telemetry on material calls. Evaluation is continuous, groundedness, recall, unit cost, not a one-time pilot slide.
L1/L2 design that integrates with ERP and existing data planes; contract and code review; observable settlement. A live network is not, by itself, a production program.
Source and smart-contract review, penetration testing, multi-party controls so no single process holds the full secret.
When the threat model requires hardware-backed or quantum-resistant keys, we design and integrate; Securosys supplies the HSM platform. Software wallets alone are not a custody architecture.
Answers with openable sources. Forecastable unit cost. Correct data residency. Reconstructible AI and chain actions. Operators who can run the system without us in the loop.
For technical reviewers: durable jobs, principal-aware storage and search, model routing by risk, eval harnesses, and logs that survive an incident postmortem.
We do not impose a monoculture. Databases, queues, model hosts, and chain clients follow your cloud, team skills, and compliance envelope. The stack page lists what we operate confidently, not a demand to replace what already works.
Can you open the evidence for an answer or a payment line? Is generation bound to retrieved context? What does a successful job cost versus a failed one? What is the escalation rate to frontier models? Those metrics decide architecture changes, opinion does not.
How production AI fits together
Most production AI pain is not the model. It is the mess around it: files with no clear source, search that ignores who may see what, answers you cannot prove, and risky steps with nobody on the hook.
We build that path with open tools people already trust (MySQL, Postgres, NATS, OpenSearch, and peers), fitted to your cloud and rules. Click any box in the diagram for what that stage does.
Click a stage
06 stages
Manuals, tickets, and feeds are cleaned and tagged with source and access rules before anything is embedded or generated. Rights start when data is written.
If you skip this
A folder dump with no tags. Search leaks or returns text nobody can source.
Cost note
Prep once. Re-index when content changes, not every user turn.
Think of it as a factory floor for answers: raw material in, durable machinery, a controlled store, a careful readout, then a human seal when the stakes are high.
Cost model (directional)
Two architectures, two columns. Path A (left): repeatedly stuff large document chunks into a public frontier API. Path B (right, highlighted): authorize, retrieve, pack, route, gate, and log on a private path. Figures are directional from field work; under NDA we walk the measurement method.
| Metric | Path A: prompt-stuffed public | Path B: evidence-first private |
|---|---|---|
| Context per answer | Large manual/project slices in the prompt | Minimal authorized evidence set with openable sources |
| Cost per successful job | Volatile; re-read same documents every turn | Typically ~50-70% below dump baselines on comparable field workloads |
| Model topology | One large remote model for all hops | Route: small/private first; frontier only when gated |
| Exact identifiers | Dense-only similarity misses IDs and clauses | Hybrid lexical + dense; deepen only when measured recall fails |
| Cost curve under load | Volume × stuffed tokens | Most traffic stays on cheap path; expensive path is exceptional |
| Vendor lock-in | Single vendor price and data path | Swappable components without rewriting the product boundary |
| Assurance | Hard to reconstruct; frequent egress of private text | Full call telemetry; VPC/on-prem generation when policy requires |
Not a price quote. Cost scales with document volume, change rate, and how often you escalate. Path A grows roughly with volume times context stuffed per call. Path B grows when retrieval quality improves and expensive inference stays the exception.
