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Solutions

AI-native operations that scale without scaling complexity.

Secular Labs designs and implements autonomous enterprise systems that combine AI agents, deterministic workflows, verification protocols, and human exception handling to automate information-intensive business operations.

From a two-week diagnostic to an ongoing build-out — Diagnose (Operational Leverage Audit), Architect (Implementation Blueprint), Scale (Architecture & Implementation)
What we do

Diagnose the leverage. Architect the change. Scale what the evidence supports.

Secular Labs diagnoses where AI-native operating leverage exists, designs the architecture required to capture it, and implements that architecture where the economics support it. The engagement can stop after diagnosis or architecture if the evidence does not justify implementation.

How we work with you

From a two-week diagnostic to an ongoing build-out.

StepStageWhat happens
01Diagnose — Operational Leverage AuditA structured gap-analysis of your current operating model against the AMEI framework's AMEA architecture — where headcount is standing in for orchestration, where verification is missing, and what growth path actually exists. The Audit does not forecast a growth multiple; it establishes the operational baseline, identifies the available leverage points, and quantifies the assumptions behind any projected benefit. It is the fastest way to determine whether the economics support moving forward.
02Architect — Implementation BlueprintWe translate the audit into a practical operating blueprint: which functions move to agent orchestration, what data and retrieval layer they need, and what governance has to sit in front of them before anything goes live.
03Scale — Architecture & ImplementationOngoing engagement where we help build out the agreed architecture — using the same open-source, self-hostable stack described in How We Work — and stay on as a fractional advisor as the system, and the organization around it, matures.

Each step is fixed in scope and can be the last one. The Audit starts from €1,300 / $1,500; a Pilot implementation of one workflow starts from €5,200 / $6,000. Want a quick start first? The AI Quick-Check is half a day with us and ends with a shortlist and a 90-day plan. See the Quick-Check →

Already know what you need built? Our applied systems put the same architecture to work on a defined operational problem, from governed agent coordination to secure tool access and self-hosted AI. See Applied systems →

See every step with pricesSee what the Audit includes

Tell us about your business and where you're seeing operational bottlenecks — we'll get back to you to begin the assessment.

Where the coordination tax is being productised

In every operational domain we look at — publisher monetisation, AR collections, logistics claims, underwriting, returns — the same structure shows up: coordination and verification still sit with people. A small number of teams have productised that layer, and now grow capacity without matching headcount.

These are market patterns, not our work. They illustrate the structural move we diagnose.

Market exampleWhat is publicly reportedWhat it tells us
A publisher-monetisation startupUnified monetisation stack, moved from services to SaaS. Publicly reported at roughly $5M ARR in about 18 months, with a founder and three engineers.The coordination tax in publisher ops can be productised, not staffed.
An accounts-receivable automation companyAgents handling invoice Q&A, collections and escalation across fragmented finance data. Its customers publicly report a ~34% reduction in days-sales-outstanding within weeks.Collections is pure coordination plus exceptions — exactly the shape agents handle well.
A sell-side ad ops startupAgentic trafficking, yield and floor management. Founded around 2023, with a publicly reported ~$5.8M Series A and a small team running millions of automated actions.Ops scales; headcount doesn't have to.
An insurance underwriting AI companyAgents for underwriting, fraud and compliance in a regulated, verification-heavy environment.Autonomy works when residual risk is bounded — architecture before AI.

Same architecture, different surface. That's what we diagnose.

See the architecture we diagnose against

Examples above are publicly reported market patterns, described without names and for illustration only. None of these companies is a client, partner or endorsement of Secular Labs, and the figures are as publicly reported, not verified by us.

What we build
What we build: autonomous workflows, verified agentic systems, AI-native operating models, AI systems due diligence
What we buildWhat it means
Autonomous WorkflowsTransform recurring analytical, operational, and coordination-intensive workflows into AI-native execution systems — the everyday work that currently absorbs headcount without needing human judgment at every step.
Verified Agentic SystemsMulti-agent architectures with deterministic validation, state controls, rollback mechanisms, and human escalation — so autonomy doesn't mean giving up control.
AI-Native Operating ModelsIdentify where AI can replace or augment recurring coordination work, and redesign the operating model around the resulting economics — not bolt automation onto an unchanged org chart.
AI Systems Due DiligenceEvaluate autonomous systems for verification quality, compute economics, model-provider dependency, governance, and operational resilience — for your own systems, or a company you're considering investing in.
See how we build it: architecture and stack
Our technology stack

A six-stage production architecture, not a demo.

Every deployment we build runs the same request lifecycle: intake, agent reasoning, retrieval, model routing, output validation, and delivery — a finite-state machine gated by deterministic verification DAGs, where every state transition must pass an invariant check before it commits. Observed end to end, and packaged to ship as one repeatable unit per client.

Six-stage production architecture: intake, reasoning, retrieval, model gateway, validation, delivery
StagePrimary toolPurpose
1. Intaken8nReceive, authenticate, validate, and classify the incoming request.
2. ReasoningLangGraphDecide what the request needs and orchestrate the steps to get there, with full state control.
3. RetrievalpgvectorFetch relevant context or memory, with source metadata attached for citation.
4. Model gatewayLiteLLM + internal gatewayRoute the completion to Gemini, Groq, or a self-hosted model, with automatic fallback — behind a gateway layer that manages prompts, safety controls, and cost tracking.
5. ValidationGuardrails AICheck structure, factual consistency, and safety before anything is released.
6. Deliveryn8nDeliver the validated response back through the original channel.

Applied across every stage.

PropertyComponentPurpose
GovernanceOPAEvaluate every proposed action against policy before it's allowed to execute, independent of the model.
InteroperabilityMCP StandardExpose business tools and APIs to agents through a standardized interface, not custom integration code per client.
ReliabilityTemporalPersist workflow state across failures and retries, so a crash mid-execution resumes rather than restarts.
Audit logNATSRecord every state transition to an append-only event log, available for inspection after the fact.
ObservabilityLangfuseTrace cost, latency and failures for every model call and every stage, so problems and spend are visible before the invoice.
Credential isolationSeparate secrets storeKeep payment and system keys outside the agent runtime. Agents never hold them; only the settlement step can use them, after every check has passed.
DeploymentDocker / KubernetesShip the whole stack as one template per client, self-hosted where data residency requires it.

We build on free and open-source tools by design, and we keep it that way as agentic tooling evolves. Every component is open-source or source-available (licenses below) and self-hostable end to end, so clients are never locked to one vendor, and data-residency requirements, including EU AI Act readiness, never force a rebuild later.

Licenses: LangGraph, LiteLLM, Temporal — MIT. pgvector — PostgreSQL License. Guardrails AI, OPA, NATS — Apache 2.0. Langfuse (core) — MIT. Docker Engine, Kubernetes — Apache 2.0. n8n — Sustainable Use License (source-available, self-hosted). MCP — open protocol specification, no license applicable.

The wider toolset we work with.

Around this core, the team works across a broad open-source toolset, chosen per project.

CapabilityTools
Agent workflows with human approvalLangGraph, LangChain, CrewAI, AutoGen, MCP / FastMCP, Pydantic AI, DSPy, Flowise, n8n
Self-hosted language modelsOllama, vLLM, LiteLLM, Llama, Qwen, DeepSeek, Mistral
Document intelligence and OCRTesseract OCR, PaddleOCR-VL, Surya OCR, olmOCR, pdfplumber
Search and knowledgeQdrant, ChromaDB, FAISS, pgvector, Elasticsearch, OpenSearch, BM25 + RRF hybrid search, Neo4j (Graph RAG), LlamaIndex, Sentence Transformers, Playwright, Crawl4AI, Firecrawl
Speech and voiceWhisper / faster-whisper, Piper TTS, Silero VAD, SpeechBrain
Computer visionOpenCV, YOLO, DeepLabV3, PatchCore
Verification, evaluation and observabilityGuardrails AI, OPA, RAGAS, DeepEval, Langfuse, PyTest, Jest
Deployment on your infrastructureDocker, Docker Compose, Kubernetes, GitHub Actions, Jenkins, Nginx, Linux (RHEL, Ubuntu), NATS, Temporal
Open-source AI: capabilities and engineering stack
Security, built into the same architecture

Every state change is treated as adversarial until proven otherwise.

The six-stage architecture above has a dedicated security extension, formalized in AMES (Autonomous Micro-Enterprise Security), the security layer of the five-part operational core. It sits between reasoning and settlement — every proposed action must independently pass a security check, on top of the functional verification already described above, before it can commit.

AMES runtime architecture: a six-layer flow extending the core architecture with ingress provenance tagging, boundary verification, and attestation/escalation before deterministic settlement

Architectural diagram from the AMES working paper. The security layers (2, 4, 5) extend the same core stages already described above (1, 3, 6) — this is an architectural model, not a production benchmark.

How it starts

Find where AI can create operational leverage.

How it starts: a structured path to measurable outcomes — 1. Diagnose, 2. Redesign, 3. Implement
StepStageWhat happens
01DiagnoseIdentify workflows, bottlenecks, and sources of unnecessary operational overhead.
02RedesignDetermine what should be automated, what should remain human, and where verification is required.
03ImplementDeploy production workflows around the highest-value opportunities.
FAQ

Frequently asked

A few questions we're asked most often before the first conversation.

What is the AMEA framework?

AMEA is the architecture layer of the AMEI framework — a research program on AI-native organizations. It defines how work moves through a business, where coordination costs accumulate, and how verification and control are enforced before automation is trusted. It is developed across 7 papers spanning architecture, economics, governance, finance, security, and market interface.

How is the Audit priced?

The Operational Leverage Audit is a fixed-fee engagement starting from €1,300 / $1,500 for one workflow. The exact fee is confirmed in writing once we understand the workflow, so it reflects the actual scope. See engagements and pricing for every step.

What makes Secular Labs different from an AI agency?

We are not an AI agency, a tool vendor, or a software subscription. The Operational Leverage Audit is a fixed-price diagnostic on one workflow that produces a decision-grade report: measured baseline, future-state design, verification architecture, and a quantified benefit model with assumptions stated. If the evidence does not support implementation, the Audit says so, and that is the deliverable. There is no obligation to proceed.

What happens if the Audit says not to proceed?

That is a valid result. If the evidence does not support implementation, the Audit still stands as the deliverable — you have a decision document rather than an open question. There is no obligation to continue to any further engagement, and delivery is not conditioned on future work.

Who is the Operational Leverage Audit for?

Founders and operators running lean teams — typically 5 to 20 people — who need to know whether a specific workflow is worth automating before committing to a larger investment. It also suits investors and fund managers evaluating AI-native targets (see our five-point technical due-diligence framework), and researchers working on operational leverage and multi-agent architecture.

How long does the Audit take?

Two to four weeks depending on access and workflow complexity. The working clock is typically 10 to 15 working days, beginning after data access. Delays in access extend the timeline on a day-for-day basis.

Does the Audit require us to buy an implementation?

No. The Audit is a standalone deliverable. If the evidence supports implementation, we provide a separate commercial proposal after the Audit presentation — as a separate document, delivered no earlier than 48 hours later, so the Audit is not influenced by what comes next. If it does not support implementation, that is the finding.