Software, automation and AI agents for firms where a wrong answer is expensive.

AI that reads. Code that

Skill Core Labs Start a pilot

Custom software, automation and AI agents, engineered for work that can’t be wrong.

Language models read the input. Deterministic code makes the decision. Every run is logged, hashed and replayable.

Built for real operations CUSTOM SYSTEMS

Software & AI systems
New York City

Operating record

Systems built across architecture, real estate, legal, field services, finance, trading and other operator-heavy domains.

Operating domainsCustom systems across distinct real-world workflows

000

Checks in one hardened release gate334 / 334 passed before production

Consequential calls left to a modelMoney, dates, doses and filings run on deterministic code

01Software

Installable mobile apps and internal platforms engineered around how an operator actually works. Designed, shipped and run in production.

Operator HQs · Scheduling engines · Dashboards 02Automation

Pipelines that ingest documents, messages and forms, apply defined rules, and return structured work, formatted outputs and decisions.

Documents · Operations · Workflows 03AI agents

Agents that operate browsers and desktop software, run research and move data between systems. Private infrastructure, sign-off gates on anything irreversible.

Browser automation · Research pipelines · Self-hosted models 04Quant & validation

Backtesting, data-integrity forensics and model validation under pre-registered rules. Evidence before capital.

Backtesting · Data forensics · Model validation

The architecture

One architecture. Every system.

A strict separation of duties between models and code. It’s why the output holds up in front of an attorney, an underwriter or an auditor.

01InterpretLanguage models turn filings, statements, voice notes and messages into structured data.

02DecideDeterministic, versioned rules make every consequential call. Same input, same answer.

03VerifyEvery run is checked, hashed and logged. Anything ambiguous halts and escalates with a reason.

Sample run logHash-verified · Replayable

SKILL CORE / RUN LOG2026-09-24 · ET

RUN 0412 · PATENT PROSECUTION14:02:11

SOURCE: Office action, 14 pp · sha256 9f3c…a41e
EXTRACT: LLM · 37 fields · min confidence 0.97
DECIDE: Rules engine v3.2 · deterministic
VERIFY: 24 / 24 checks · 0 overrides
OUTPUT: Response shell → attorney queue · 41 s

RUN 0413 · UNDERWRITING14:06:48

SOURCE: Bank statements, 6 mo · sha256 07be…19c2
EXTRACT: LLM · 58 fields · 1 below threshold (0.71 < 0.90)
DECIDE: Halted before rules engine
VERIFY: 11 / 12 checks
OUTPUT: ■ ESCALATED → underwriter, reason attached

SOURCE HASH · MODEL · RULES VERSION · CHECKS · OUTCOMESAMPLE

Operating doctrine

Adapted from quantitative research

Models interpret. Code decides.

Every consequential call runs in deterministic, versioned code.

Fail closed.

Ambiguity halts the pipeline. The system escalates with a coded reason instead of shipping a plausible error.

Replayable by default.

Source hash, model, rules version and checks are recorded for every run.

Autonomy is earned.

Agents unlock authority only against measured reliability. Irreversible actions require sign-off.

Pre-registered acceptance.

Pass criteria are fixed before the build. Validation runs on held-out data.

Capability stack

In production and in active development

AInterfaces

Installable mobile apps, WhatsApp and Telegram operations, push and email. Deployed where the work already happens.

BModels

Orchestration across OpenAI, Anthropic and local open-weight models, routed by task and cost.

CData

Extraction from PDFs, scans, bank statements, transcripts and market feeds into structured, validated records.

DInfrastructure

Edge deployment on Cloudflare and Supabase, or fully self-hosted on client hardware inside a private network.

EAssurance

Automated regression suites, held-out validation sets and hash-verified audit trails.

Engagements

Every build starts with a paid pilot

Pilot

A production-grade first release on one live workflow. Fixed scope, fixed decision date, success criteria agreed up front. Fee credited to the build.

Build

Full delivery against the pilot’s acceptance criteria: architecture, integrations, test suites and deployment.

Operate

Monitoring, maintenance and incident response on a monthly retainer. New scope quoted separately.

Advisory

Architecture and adversarial review for teams putting AI into production.

Contact

Start with one workflow.

Send the process, what goes in, and what a mistake costs. You’ll get a straight answer on feasibility, the architecture, and what a pilot would include.

hello@skillcorelabs.com