Add LLMs and tool use to your product or internal systems, including MCP servers that give agents controlled access.
Discipline: AI Agents
Working term: Senior people you meet are the people doing the work
Formats: ongoing monthly work or a single audit
Who you talk to: the senior lead, on every weekly call
The service
Our approach to LLM & MCP Integration
We integrate model APIs and tool use to production standards, with streaming, structured outputs, function schemas, caching and rate-limit handling. We also build MCP servers so agents can reach your data and actions through a typed interface you can audit.
Before work starts, we agree on the number that matters most to you. Each sprint is planned against it. You get a short recorded update every week and a dashboard you can open any time, and the senior lead joins every call.
A senior lead runs your account from day one
A recorded walkthrough each week and a shared reporting dashboard
We work inside the tools and CMS your team already uses
Target ranges written into the agreement before we begin
Our Process
Four stages of LLM & MCP Integration
The same order every time: find the constraint, plan, deliver, then review. Here is what each stage involves.
01
Find the constraint
We record a baseline for LLM & MCP Integration, look for what is holding results back, and write down the numbers you will judge us by.
02
Put the work in order
A senior lead drafts a 90-day plan. Fast fixes come first; the slower, harder-to-copy work is scoped to follow them.
03
Deliver every week
Work goes live in weekly batches. Tracking and reporting are kept current against the targets you approved at the start.
04
Review and adjust
Once a month we compare results to the plan and change course where the data says to. You can see where each dollar went.
Adding a language model to a product involves more than a prompt. Context management, retrieval, streaming responses, error handling, fallbacks and evaluation all affect whether the feature works. We engineer each part so the feature behaves well inside your shipped product, on the web or in your app.
Good engineering turns an AI idea into a feature people trust.
Prompts, context and retrieval designed carefully
Streaming interfaces, error handling and fallbacks
Reliability work for AI features
Features ready for real users
02
How progress tends to build
Commercial terms usually move later than technical fixes
Cost & performance
Keep it fast and affordable at volume
Model choice, caching, prompt length and routing all drive cost and speed. We match each task to the smallest model that does it well, cache repeated work, trim prompts and route requests sensibly, so the feature stays quick and affordable as usage grows.
That work is what makes an AI feature viable beyond the pilot.
Right-sized models and routing
Caching and shorter prompts
Cost control as usage grows
Latency tuning
03
Content velocity
Briefed, drafted, edited, shipped
Safe & secure
Guardrails, testing and careful data handling
AI features that produce bad output or leak data hurt your product and your reputation. We add guardrails for quality and safety, build tests from real examples and design data handling around your privacy obligations, including PIPEDA, Quebec’s Law 25 and any sector rules you follow. Where needed, we use providers and regions that keep data in Canada.
With those in place you can put the feature in front of customers with confidence.
Quality and safety guardrails
Tests built from real examples
Privacy-conscious data handling
Canadian hosting options where required
Our working terms
What a LLM & MCP Integration engagement includes
These describe how we staff, report on and scope the work. They hold from the first week, before any result exists to point at.
A named senior specialist leads each account
Progress shown in live Looker Studio dashboards
Measured on leads and revenue
Senior
People you meet are the people doing the work
Written
Scope and price agreed before work starts
Weekly
Progress updates in plain language
CAD
Scoped and invoiced in Canadian dollars
FAQs
Before you sign: common questions
Straight answers on scope, timing and how we report.
What does LLM integration involve?
Designing prompts, context and retrieval, building streaming interfaces, handling errors and fallbacks, controlling cost and adding guardrails, testing and secure data handling. Together those decide whether an AI feature works.
How do you keep model costs under control?
We use the smallest model that does each task well, cache repeated work, keep prompts lean and route requests sensibly.
How do you keep the feature reliable?
With guardrails, tests built from real cases and fallback behaviour, so it acts predictably and can be improved over time.
How do you handle privacy?
We minimize the data sent to models, choose providers with suitable data terms and can keep processing in Canadian regions, in line with PIPEDA and provincial requirements.
Tell us about the business and a senior specialist will get back to you within one business day. If starting with AI Agents looks like the wrong first step, we will say so and suggest what to fix first.
