Agentic AI · Infrastructure · Studio

Ship AI
that reasons,
plans, and acts.

We design, deploy, and operate multi-agent workflows for SMBs. Production-grade orchestration, RAG pipelines, and real-time tool use. No demo-ware.

Book discovery → Explore solutions

Routine ticket reduction

80%

via grounded copilots and agent escalation

Average uplift

3.4×

throughput per ops headcount, post-deploy

Discovery → prod

4–8wk

typical time to first live agentic system

orch.acceleratech.ca / live

view · graph

§ 01 · live · stream

Live agent events / sample stream

live · stream/agent.events tps · 32.5k/min

+612ms reasoning-02 · reflect → self/eval 209 tok 97ms

+611ms research-01 · retrieve → pgvector://docs 439 tok 80ms

+610ms research-01 · retrieve → pgvector://docs 372 tok 58ms

+609ms research-01 · retrieve → rag://kb-internal 836 tok 126ms

+608ms execute-03 · tool.call → api://stripe 45 tok 164ms

+607ms guard-00 · redact → pii/email 28 tok 20ms

+606ms execute-03 · tool.call → api://stripe 97 tok 199ms

+605ms guard-00 · redact → pii/email -4 tok 8ms

+604ms guard-00 · redact → pii/email -25 tok 8ms

+603ms reasoning-02 · route → agent/execute-03 79 tok 10ms

+602ms research-01 · retrieve → pgvector://faq 354 tok 60ms

+601ms research-01 · retrieve → rag://kb-internal 817 tok 88ms

§ 02 · stack · model-agnostic by design >> 20+ integrations · zero lock-in

OpenAI /LLM

Anthropic /LLM

Mistral /LLM

Llama /LLM

LangGraph /orch

CrewAI /orch

n8n /orch

Pinecone /vector

pgvector /vector

Weaviate /vector

Qdrant /vector

Supabase /infra

Vercel /infra

Cloudflare /infra

AWS /infra

HubSpot /biz

Stripe /biz

Notion /biz

Slack /biz

Zapier /biz

OpenAI /LLM

Anthropic /LLM

Mistral /LLM

Llama /LLM

LangGraph /orch

CrewAI /orch

n8n /orch

Pinecone /vector

pgvector /vector

Weaviate /vector

Qdrant /vector

Supabase /infra

Vercel /infra

Cloudflare /infra

AWS /infra

HubSpot /biz

Stripe /biz

Notion /biz

Slack /biz

Zapier /biz

§ 03 · about · the studio

Built for the age of autonomous systems.

Acceleratech is a small, senior team designing agentic systems that reason about your domain, plan multi-step actions, and execute against real APIs, with the guardrails to ship them in production.

We work end-to-end: from discovery and architecture to evals, observability, and the long-tail of model drift after launch. Each engagement is sized to the outcome, not the timeline.

Model-agnostic architecture

OpenAI, Anthropic, Mistral, Llama, selected per workload, swappable behind a unified gateway.

Retrieval-augmented by default

Every agent grounds output in your proprietary data via RAG, with citations and audit trails.

Governance & compliance-ready

PII redaction, role-based access, evals harness, and SOC 2-aligned deployment patterns built in.

Operate, not just deploy

We run drift monitors, track per-agent cost and latency, and ship regression tests with every model swap.

§ 04 · services · what we build

AI that works /
not just demos well.

SVC · 01 deployed

Conversational Copilots

Context-aware assistants grounded in your knowledge base via RAG, function calling, and tool use. Customer-facing or internal.

p50 lat

420ms

tools

12+

modes

LLMRAGtools

SVC · 02 deployed

Multi-agent Workflows

Autonomous agents that plan, delegate, and execute multi-step tasks across systems. LangGraph, CrewAI, custom runtimes.

agents

∞

depth

8 hops

guard

orchplanningtools

SVC · 03 deployed

Document Intelligence

Extract, classify, and route structured data from unstructured docs using vision-language models. Zero manual entry.

acc

97.4%

fmts

cost

$0.02

VLMOCRIDP

SVC · 04 deployed

Predictive AIOps

Demand forecasting, churn, predictive maintenance, deployed as real-time APIs with explainability layers.

mape

4.1%

win

+12%

sla

99.95

MLOpsXAIts

SVC · 05 deployed

Custom LLM Tuning

Domain adaptation via instruction tuning, RLHF, DPO. Includes evals harness with automated regression testing.

evals

120+

wins

+18%

reg

auto

SFTDPOevals

SVC · 06 deployed

AI Infrastructure

API gateway, vector DB selection, embedding strategy, latency optimization, and cost governance. End-to-end.

cost↓

42%

p99

<1.2s

envs

infravectorops

§ 05 · roi · why it matters

Measurable lift / immediate ROI.

ticket deflection

80%

of routine inquiries resolved without human intervention

opex reduction

30%

average reduction in customer support OpEx

profitability

72%

of SMBs report AI improving profitability within 12 months

time to prod

4–8wk

discovery call to live production deployment

§ 06 · process · how we engage

Discovery to prod in four moves.

Discover

30-min mapping call. We identify high-leverage workflows and constraints. You leave with a written diagnosis.

week 0 · free

Scope

Architecture sketch, model selection, eval criteria, success metrics. Fixed-price proposal.

week 1

Build

2-week proof-of-concept on real data. Hands-on demos, weekly checkpoints, rollback at any time.

week 2–4

Operate

Production rollout with monitoring, drift alerts, and a quarterly evals review baked in.

week 4–8 →

§ 07 · faq · the questions you asked

Common / uncommon questions.

What exactly is agentic AI?

Standard chatbots follow decision trees. Agentic AI uses LLMs as reasoning engines that plan sequences, call tools, evaluate results, and self-correct autonomously. Closer to a digital employee than a FAQ widget.

Do I need to share data with a model provider?

Not necessarily. We architect with RAG, keeping your data in your own vector store. For sensitive workloads we deploy open-source models (Llama, Mistral) entirely on-premise or in your private cloud.

How is this different from RPA?

Traditional RPA is brittle: UI changes break it, and it can't handle ambiguity. Agent workflows understand intent, handle exceptions, and adapt. Faster to build, cheaper to maintain.

How do you handle hallucinations?

Grounding via RAG, structured output parsing with schema validation, confidence scoring, automated evals that flag regressions, and human-in-the-loop gates on high-stakes decisions.

Suitable for SMBs, or only enterprise?

SMBs are our core focus. We use API-first, serverless patterns that scale lean. Most clients see positive ROI within the first quarter without needing an in-house data team.

What does engagement look like?

30-min discovery, scoped proposal, 2-week PoC, 4–6 week production build, monitoring plus drift alerts. Fixed-price, milestone-based.

§ 08 · contact · let's build

Ready to deploy real AI?

Tell us about your
biggest bottleneck.

We'll show you what's possible with agentic AI. No jargon, no pressure, just a clear picture of the ROI.

email [email protected]

tel +1 (418) 476-4606

hours Mon–Fri · 09:00–17:00 ET

based Quebec, Canada

typical response

under 4h on weekdays

Ship AI that reasons, plans, and acts.