What we build
Production-grade infrastructure. Not a model wrapper.
Each capability is a reliability primitive — orchestration, eval, audit, drift, registry, fallback. Composed into one infrastructure layer that every agent and workflow runs on.
Multi-model orchestration
Claude, GPT, Gemini, and open-source models composed per use case — picked at runtime based on cost, latency, and reasoning depth. Auto-fallback when a provider goes down; auto-escalate to a stronger model when confidence is low.
Eval harness · golden-set testing
Every prompt change runs against a golden set before promotion. Regressions caught pre-deploy; improvements quantified; rollback ready. Promotions through your existing PR review process — never an unreviewed change in production.
Audit log · per-decision
Every model call, tool call, and decision logged with input, output, model version, prompt version, latency, and confidence. Replayable per agent, per workflow, per record. Compliance-grade trail without bolting it on.
Drift + anomaly monitoring
Per-model output distributions tracked against rolling baselines. Score drift, classification skew, latency spikes flagged before customers notice. Auto-pause writes; engage fallback; page on-call with the runbook attached.
Prompt + version registry
Every prompt, template, and tool config version-controlled. Roll back regressions in seconds; A/B candidate versions in production traffic; pin and promote per agent. Never edit live in a vendor UI with no audit trail.
Confidence-thresholded gates
Per-decision confidence thresholds with escalation paths. Above the threshold runs solo; below it escalates to a stronger model, a deterministic fallback, or a human approval queue — depending on your runbook for that decision class.
Where infrastructure earns its keep
The moments that distinguish prototype from production.
Provider outage, drift detection, eval gate, escalation path, compliance audit, cost optimisation. Same infrastructure handles all of it — composed from shared primitives, not stitched together per agent.
Multi-provider reliability
Claude unavailable? Auto-fallback to GPT or Gemini with the same prompt format. Provider returns garbage? Drop down to a deterministic fallback. Your agents never go offline because one vendor had a bad day.
Pre-deploy eval gates
New prompt versions run against the golden set before they touch production traffic. Regressions caught before customers see them; improvements quantified; rollback always one command away. CI for prompts.
Drift response runbook
Score drift detected on the ICP scorer? Auto-freeze writes, engage deterministic fallback, page on-call with the runbook attached. Containment first; root-cause investigation after — not the other way around.
Confidence-thresholded escalation
Low-confidence calls escalate up the model ladder — Sonnet → Opus → human review queue. Per-decision-class thresholds tunable per dollar value, deal stage, or compliance tier. Never silently degrade.
Compliance audit trail
Every decision logged with model, prompt, retrieved context, tool calls, confidence. Replayable by compliance reviewers; per-recipient redaction enforced; PDPA/GDPR-aligned residency options. Audit-ready by default.
Cost + latency optimisation
Per-call routing optimised against your cost ceiling and latency budget. Cheap models for low-stakes calls, premium models reserved for high-confidence-required decisions. Telemetry shows the trade-off live.
Model families we deploy
No single model handles every reliability concern. So we compose.
Routing, eval, drift detection, and confidence thresholding each run on their own model — composed into one infrastructure layer with version control at every step.
Picks the model per request based on cost, latency, reasoning depth, and current provider health. Auto-fallback when one provider returns errors; auto-escalate to a stronger model on low confidence. Deterministic policy, fully observable.
Runs every prompt candidate against your golden set before promotion. Per-case pass/fail with diffs from production version. CI-style — promotion gated on regression count, not just average pass rate.
Statistical + ML models running per agent against rolling output baselines. Score drift, classification skew, latency spikes detected with confidence scores. Tunable thresholds per agent class and risk band.
Per-decision threshold model that decides whether the call runs solo, escalates to a stronger model, falls back to deterministic logic, or pauses for human approval. Trained on your historical handoff data.
Components wired into every agent
Every layer of the AI reliability stack — composed.
Multi-provider routing, golden-set eval, audit logging, prompt registry, fallback paths, alerting. Composed into one infrastructure that every agent and workflow runs on.
Per-decision explainability
Every decision carries its full trail. For ops. For audit.
Model used, prompt version, retrieved context, tool calls, latency, confidence — captured per call. Operators replay any decision step-by-step. Compliance reviewers see exactly what happened, when.
- Model + prompt version on every call
- Retrieved context + tool calls captured
- Confidence + latency per decision
- Replayable from any historical state
Frameworks we align to
Why Axccelerate for AI infrastructure
Not a model wrapper.
An infrastructure system.
A model wrapper gives you an API call. Our system gives you orchestration, eval, audit, drift detection, registry, and escalation gates — the layer that turns AI from a demo into production infrastructure.
Pricing
Priced to your fleet and your stack — not seat counts.
Infrastructure deployments are scoped — we cost against your agents, integrations, and review cadence before quoting.
Glossary
The vocabulary behind every reliable AI fleet.
A quick reference for the terms that show up in infrastructure specs, runbooks, and incident reviews — the language your platform, AI, and ops teams will use during deployment.
- LLMOps
- LLM operations discipline
The discipline of running large language models in production — orchestration, observability, eval, drift monitoring, version control. Like DevOps, but for AI behavior.
- Model router
- Per-call model picker
The component that decides which model handles each request — Claude, GPT, Gemini, or self-hosted — based on cost, latency, capability fit, and provider health. Auto-fallback when a provider fails.
- Eval harness
- Pre-deploy testing system
The CI-style system that runs every prompt candidate against a golden set before promotion. Catches regressions, quantifies improvements, gates production releases on pass-rate metrics.
- Golden set
- Curated test cases
A curated set of input/expected-output pairs that represent the expected behavior of an agent. New prompt and model versions are scored against the golden set before promotion.
- Drift
- Output-distribution shift
When a model's outputs gradually change shape — score skew, classification distribution shift, latency creep — usually due to upstream data or behavior changes. Drift monitoring catches it before it cascades.
- Confidence threshold
- Solo-vs-escalate boundary
The score above which a model call runs solo, below which it escalates to a stronger model, deterministic fallback, or human approval. Tunable per decision class, dollar value, and risk band.
- Fallback
- Deterministic safety net
A non-AI path that the system can degrade to when models are unavailable, unconfident, or producing garbage. Fallbacks are tested in golden-set evals alongside the primary path.
- Prompt registry
- Versioned prompt store
A version-controlled store of every prompt, template, and tool config — typically backed by Git. Promotion through your PR review process; rollback via git revert; A/B candidates in production traffic safely.
- Audit log
- Per-decision record
The complete record of every model call — input, output, model version, prompt version, retrieved context, tool calls, latency, confidence. Available for replay, compliance, and tuning.
- Routing policy
- Per-call selection rule
The rule set that drives the model router — cost-optimal, latency-optimal, or capability-tier-aware. Tunable per agent and per use case; observable in the audit trail.
- Approval gate
- Mandatory human checkpoint
A step that always requires named human sign-off — typically used on irreversible actions, high-dollar decisions, or off-script edge cases. Threshold tunable per decision class.
- Observability
- Per-step instrumentation
The metrics, traces, and logs that make agents inspectable while they run. Cost, latency, accuracy, drift — all surfaced live, not after the fact.
Run AI in production.
Sleep through the night.
30-minute scoping with a senior platform engineer. You'll leave with an infrastructure map, integration plan, and realistic timeline — not a sales pitch.