AI CONSULTING

AI that works for businesses your size

Practical AI implementation for businesses doing $500K–$50M annually, delivered by a team that runs the same infrastructure internally. Not enterprise consulting at enterprise prices.

Book AI Assessment How we approach AI

What we do

The gap between hype and implementation is where most SMBs get stuck.

SMBs face three bad options: do nothing, pay $50K+ for a strategy deck, or piece together AI tools and call it transformation.

We implement AI for businesses with 20 to 500 employees, on the same infrastructure that runs our own operations, with measurable ROI tied to your business outcomes.

AI Operations

Running Agents

telos-support-v2 Live 142 req/hr

invoice-classifier Live 89 req/hr

doc-summarizer Standby —

Model Endpoints

llama-3.1-70b GPU On-prem

embed-v3 CPU Cloud

Hybrid

Deploy

24/7

Monitor

Yours

Ownership

GPU utilization34%

Pipeline throughput2,847 / day

Core capabilities

From assessment to autonomous agents, every tier builds on the last.

AI Readiness Assessment

Find where AI actually pays off in your business.

Workflow audit, data inventory
90-day roadmap, quick wins
Cost-benefit, not a deck

Workflow Automation

Configured for your existing tools, tested with real data.

Email triage, invoice processing
Lead scoring, content pipelines
Staff trained on each

AI Agent Deployment

Agents that do the work, wherever you host them.

Memory and autonomous tasks
Cloud, on-prem or hybrid
Scoped access, human escalation

Fractional AI Operations

Telos One as your outsourced AI department.

Agents configured for you
Continuous optimization
Quarterly strategic reviews

Governance & Data Sovereignty

Frameworks that actually get followed.

Acceptable use, data classification
Approved tools, monitoring
PIPEDA and provincial rules

Integration & Training

Wired into your tools, with your team trained.

CRM, accounting, e-commerce
Hands-on training, videos
30-day support after launch

AI Readiness Assessment

Find where AI actually pays off in your business.

Workflow audit, data inventory
90-day roadmap, quick wins
Cost-benefit, not a deck

Workflow Automation

Configured for your existing tools, tested with real data.

Email triage, invoice processing
Lead scoring, content pipelines
Staff trained on each

AI Agent Deployment

Agents that do the work, wherever you host them.

Memory and autonomous tasks
Cloud, on-prem or hybrid
Scoped access, human escalation

Fractional AI Operations

Telos One as your outsourced AI department.

Agents configured for you
Continuous optimization
Quarterly strategic reviews

Governance & Data Sovereignty

Frameworks that actually get followed.

Acceptable use, data classification
Approved tools, monitoring
PIPEDA and provincial rules

Integration & Training

Wired into your tools, with your team trained.

CRM, accounting, e-commerce
Hands-on training, videos
30-day support after launch

The AI stack

Every layer of a working AI system, and where we stand on each.

A demo is one layer. A system that runs in your business is nine. This is how we think about each of them, and the tools we reach for.

Layer 01 of 09

Models

The reasoning engine: frontier APIs or open-weight models you host yourself.

We work with

OpenAI
Anthropic Claude
Google Gemini
Meta Llama
Mistral
Cohere
Hugging Face
Ollama
vLLM

Our position

Chosen per workload, not per vendor. Frontier APIs where the task justifies the cost and the data may leave; open-weight models on your hardware where it may not. We build so the model is a configuration value, never an architecture decision.

Layer 02 of 09

Agents and orchestration

Multi-step work that uses tools, hands off between steps and knows when to stop.

We work with

LangGraph
OpenAI Agents SDK
PydanticAI
CrewAI
Microsoft AutoGen
Semantic Kernel
Google ADK
AWS Bedrock Agents

Our position

Agents earn their keep on bounded, repeatable jobs with a human escalation path. We favour typed, inspectable frameworks over autonomous swarms, and the dashboard ships with the agent.

Layer 03 of 09

Retrieval

Grounding answers in your documents and data instead of the model's memory.

We work with

LangChain
LlamaIndex
Haystack
DSPy
GraphRAG
Unstructured

Our position

Most "AI on our documents" projects fail at ingestion, not at the model. Document quality, chunking and access control come first, then retrieval, then generation. The evaluation set is written before the first prompt.

Layer 04 of 09

Embeddings and vector storage

How meaning is indexed and searched.

We work with

OpenAI Embeddings
Cohere Embed
Voyage AI
BGE
Sentence Transformers
pgvector
Pinecone
Weaviate
Qdrant
Milvus
Elasticsearch
MongoDB Atlas

Our position

If you already run Postgres, pgvector is usually the right first answer. A dedicated vector database comes in when scale or latency demands it, not because a slide says so.

Layer 05 of 09

Tool access

How a model reaches your CRM, ticketing, files and database safely.

We work with

Model Context Protocol
MCP SDK
FastMCP
GitHub MCP
Slack MCP
PostgreSQL MCP
Google Drive MCP
Custom MCP servers

Our position

MCP lets us give a model scoped access to your systems without bespoke glue for every tool. Our servers run with least-privilege credentials and every call is logged, so access can be reviewed like any other integration.

Layer 06 of 09

Guardrails and AI security

Keeping inputs, outputs, data and spend inside policy.

We work with

NVIDIA NeMo Guardrails
Guardrails AI
Microsoft Presidio
Lakera Guard
Prompt Security
Protect AI
Azure AI Content Safety
AWS Bedrock Guardrails

Our position

Prompt injection, data leakage and unbounded spend are the three failure modes that reach the board. PII redaction, input and output policies and per-agent budgets go in before launch, under the same governance as the rest of your security program.

Layer 07 of 09

Observability and evaluation

Knowing what the system did, what it cost, and whether it was right.

We work with

LangSmith
Langfuse
Arize Phoenix
Weights & Biases Weave
TruLens
Ragas
Promptfoo
Helicone

Our position

Every deployment ships with tracing, an evaluation set that runs on each change, and a cost line per feature. No trace, no production.

Layer 08 of 09

Memory and state

What the system remembers between sessions, and for whom.

We work with

Mem0
Zep
Letta
LangGraph memory
Redis
PostgreSQL
Neo4j

Our position

Memory is a data-governance question before it is a feature: what is retained, for whom, for how long, under which retention policy. We default to your existing database with explicit schemas over opaque memory services.

Layer 09 of 09

Automation and workflow

Where AI plugs into the business process, and who owns it afterwards.

We work with

n8n
Make
Zapier
Microsoft Power Automate
Temporal
Apache Airflow
Prefect
Kestra

Our position

n8n or Power Automate where a workflow is simple and your team should own it. Temporal or Airflow where it must be durable and auditable. Code when the process is the product.

Who this is for

Organizations ready to move beyond the hype into measurable AI adoption.

Know You Need AI, Don't Know Where to Start

Plain language: "this saves twelve hours a week", not "transformer architecture".

Shadow AI Is Already Happening

Staff already use AI tools with company data. Approved alternatives with guardrails, before it becomes a breach.

Ready to Compete with Bigger Firms

A 30-person company punching like 300: production-grade builds with monitoring, fallbacks and governance.

Know You Need AI, Don't Know Where to Start

Plain language: "this saves twelve hours a week", not "transformer architecture".

Shadow AI Is Already Happening

Staff already use AI tools with company data. Approved alternatives with guardrails, before it becomes a breach.

Ready to Compete with Bigger Firms

A 30-person company punching like 300: production-grade builds with monitoring, fallbacks and governance.

Find out what practical AI looks like for your business.

24 hr response time
0 commitment required
100% North American team

Book AI Assessment How we approach AI