Blue Abraham

BP-02 · Technical deep dive

A token factory you own, end to end.

Four owned layers. One swappable model bus. The token factory is a thin, model-agnostic architecture that sits between your application and whichever frontier model wins this quarter — so swapping providers becomes a router-config change, not a rewrite.

The blueprint

Four owned layers. One swappable model bus.

A request enters at the top and traces a single path: context is assembled, the prompt is compiled, the router selects a provider, and the eval harness gates the response.

The boundary on the right is the only contract that touches a vendor. Everything left of it — context, prompts, routing policy, evals — is yours.

Token factory reference architectureA four-layer architecture diagram. An application request enters at the top and flows down through a context engine, prompt compiler, model router, and eval harness, before returning a response. The model router fans out to three swappable providers — Anthropic, OpenAI, and open-weight models — separated from the owned layers by a vendor boundary line.BP-02 · ARCHITECTUREREFERENCE v2.4OWNEDSWAPPABLEAPP · REQUEST01Context engineingest → embed → retrieve02Prompt compilerintent → template → tokens03Model routerroute(task, cost, SLA)04Eval harnessreplay → score → gateAPP · RESPONSEAnthropicOpenAIOpen weightsBoundary line:swap at the router,not the app.BLUE ABRAHAM · TOKEN FACTORY · BP-02SCALE 1:1 · 2026
Fig. 01 — Reference architecture, request lifecycle

The four layers

Each layer is a contract, not a framework.

Every layer publishes a typed input and output. Replace the implementation; the contract — and the application above it — stays put.

  1. 01

    Layer

    Context engine

    ingest(source) → embed → retrieve(query)

    Your proprietary context graph. Owned schema, portable embeddings, no vendor-specific retrieval API. Built so the same retrieval call works whether the downstream model is Claude, GPT, or a quantised open-weight running on your own GPUs.

    In
    source documents, telemetry, structured records
    Out
    ranked context chunks + provenance

    You own

    • schema
    • embeddings
    • retrieval policy
    • provenance
  2. 02

    Layer

    Prompt compiler

    compile(intent, context) → ProviderPayload

    Declarative prompt definitions compiled to provider-native payloads. One source of truth, many targets — Anthropic message blocks, OpenAI chat completions, raw chat-template tokens for self-hosted models. Versioned, diff-able, code-reviewed.

    In
    intent + retrieved context
    Out
    provider-native request payload

    You own

    • prompt source
    • templates
    • tool schemas
    • version history
  3. 03

    Layer

    Model router

    route(task, cost, SLA) → Provider

    Per-task routing across providers. Policy lives in code: route long-context summarisation to one provider, structured extraction to another, latency-critical traffic to a self-hosted model. Swap providers by changing a routing rule, not the application.

    In
    compiled payload + task metadata
    Out
    provider response (raw)

    You own

    • routing policy
    • fallback chains
    • rate-limit budget
    • cost ledger
  4. 04

    Layer

    Eval harness

    gate(response, golden) → {pass, score, drift}

    Your golden evals gate every model swap. Replays a frozen set of representative requests against any candidate provider, scores them on your rubric, blocks deploys that regress. Independence you can measure, not just promise.

    In
    candidate response + golden set
    Out
    pass/fail + drift report

    You own

    • golden set
    • scoring rubric
    • drift baseline
    • release gate

Request lifecycle

One trip through the factory.

A trace of a single typed intent — from application call to gated response — at sub-second wall-clock. Every step is observable; every decision is loggable.

  1. T+0ms

    App emits intent

    The application calls the factory with a typed intent — `summarise(contract)` or `extract(invoice)`. No model name, no provider SDK.

  2. T+12ms

    Context engine retrieves

    The retrieval call hits your owned vector index plus structured stores. Returns ranked chunks with provenance, never the raw documents.

  3. T+18ms

    Prompt compiler binds

    Intent + retrieved context are bound to the active prompt template, then compiled to the payload shape the chosen provider expects.

  4. T+22ms

    Router selects provider

    Routing policy reads the task class, the cost ceiling, and the SLA. Picks one provider and a fallback chain. Logs the decision.

  5. T+820ms

    Provider responds

    Raw provider response returns. The factory normalises tool calls, citations, and stop reasons into a provider-agnostic shape.

  6. T+830ms

    Eval harness gates

    Inline evals run on the response — schema validation, refusal detection, drift checks. Fail-closed by default; fall through to the next provider in the chain on regression.

What you keep

Six dimensions of independence — six places vendor SDKs leak.

Independence is not a posture; it is a list of artifacts. If you can’t name what you own at each row, you don’t own the stack.

Dimension

Vendor SDK

Token factory

Prompt source of truth

Lives in vendor playground or notebook

Versioned in your repo, code-reviewed

Context store

Vendor-managed embeddings + retrieval API

Owned schema, portable embeddings

Routing policy

Hard-coded to one provider's model family

Per-task routing across providers

Eval set

Vendor benchmarks; no replay on swap

Golden set replayed on every candidate

Cost ledger

Monthly invoice; no per-task attribution

Per-route, per-tenant attribution

Exit cost

Re-implementation across the application

Routing-rule change + eval replay

Where regulation meets architecture

The four layers map onto what EU regulators already ask for.

DORA, NIS2, GDPR, and the EU AI Act are substantive in this work — not decoration. The token factory is not a compliance service. It is an architecture that happens to produce the artefacts a compliance conversation needs: data residency, auditability, vendor independence.

Framework

In scope

What the factory produces

DORA

ICT risk + third-party dependency, financial entities

Router-layer visibility into every provider call, per-route cost ledger, documented fallback chain — the artefacts an ICT-risk review asks for.

NIS2

Supply chain security, essential & important entities

Owned context store and owned prompts mean no hidden third-party processor inside the critical path. Retrieval and evals run on infrastructure you can account for.

GDPR

Personal data processing + cross-border transfer

Context engine enforces residency and provenance per chunk. The prompt compiler stops PII from drifting into vendor-hosted prompt history by making the payload code-reviewed, not free-text.

EU AI Act

High-risk AI systems, transparency, post-market monitoring

The eval harness is the post-market monitoring loop: golden replays, drift baselines, release gates — the evidence a conformity assessment is looking for.

Blue Abraham does not sell a compliance product. It builds architecture that makes the conversation with your compliance and legal stakeholders shorter.

Next step

Start with an architecture review.

A bounded engagement. I walk your current stack against the four-layer reference, name what you already own, and write up where the architecture is sound and where it is at risk. One to three weeks, fixed price, delivered in writing.

Get in touch