I'm Gagan — an AI consultant and agentic engineer in Brantford, Ontario. This is an AI shop and nothing else: multi-agent orchestration, the loops and memory underneath them, MCP tool surfaces, and the evals that decide whether any of it ships — for teams in Paris, Brant County, and across Ontario.
Nine AI builds — multi-agent orchestration, agent loop engineering, context engineering with retrieval and memory, MCP tool surfaces, evals and guardrails, in-product copilots, voice agents, document intelligence and AI strategy — shipped end to end by one AI specialist in Brantford, Ontario. Every engagement is custom; these are the shapes they usually take.
A working session to map every manual step in the workflow. I leave with a list of candidates ranked by time-saved-per-dollar.
A node-graph of your target system with every handoff, failure mode, and data contract named. You approve before anything runs.
Narrow vertical releases every 3–5 days. You see the system working on your data before it's fully finished.
Monitoring, alerts, and iteration after launch. A monthly retainer keeps the system sharp as your business shifts.
How work actually moves through the studio: specialised agents pick tickets off a kanban queue, build and test inside isolated sandboxes, and a human reviews before anything ships. Three of the workflows, mapped end to end.
Failures never stall the pipeline — every red test loops the work back to the Build Agent until the whole chain is green.
Multi-agent systems, retrieval and memory, voice agents and document intelligence — shipped for teams across Ontario and beyond.
RAG over 40k help-center articles and historical tickets, routed through a Claude agent with a strict escalation policy and deflection scorecard.
Clay + GPT-4 + HubSpot. Inbound forms scored, researched, and booked in under 90 seconds — no SDR involvement.
Structured generation with brand-voice guardrails, human review queue, and Figma handoff. Replaced a four-person content pod.
Ingest any PDF, extract obligations, flag risk clauses against a firm-specific playbook. Citations back to the source paragraph.
A Slack-native agent with access to 11 internal tools. Fuzzy requests resolve into structured actions with a confirmation step.
A planner fans each stuck shipment out to subagents for carrier, customs and inventory, then merges their findings into one action. Durable execution means a mid-run deploy resumes instead of restarting.
Turn detection and barge-in tuned on the clinic's own recordings, booking straight into the practice system. Low confidence or anything clinical transfers to a person with the transcript attached.
A real agent handling an inbound lead — trigger fires, agent reasons, actions dispatch to HubSpot, Notion, and Linear. The kind of thing that used to take a person eleven minutes.
“I've worked closely with Gagan, and his ability to turn complex AI concepts into real, working products is exceptional. He doesn't just understand the tech — he knows how to execute at scale.”
“Gagan's technical expertise and leadership are truly exceptional. His deep knowledge of AI systems and product engineering, combined with his ability to manage complex projects, makes him a standout CTO.”
“I'm deeply impressed with Gagan and his team's expertise and dedication they showed working on our project. With their help and support, I could finally see my vision turn into reality.”
Gagan Deep Singh — an AI consultant and agentic engineer based in Brantford, Ontario, with fourteen years of engineering leadership behind the studio.
I spent fourteen years scaling engineering teams before I noticed most of the interesting engineering was happening just outside the product — in the seams between tools, inboxes, and spreadsheets where work actually got done.
Buildwithgagan is a deliberately small studio. One person, end-to-end, every engagement. No account managers, no junior handoffs, no "creative director" layer between you and the work. The person on the first call is the person writing the code and the person answering Slack at 11pm when something needs a patch.
The studio is currently full, so new work joins a waitlist rather than a start date. That has always been the shape of it — I turn down more than I take, usually because the problem is better solved by a process change than by software, or because a bigger shop is genuinely a better fit. I'll tell you which one you are on the first call, waitlist or not.
This is an AI shop and nothing else. Not a general agency with an AI page — the whole practice is agent orchestration, the loops underneath them, retrieval and memory, MCP tool surfaces, and the evals that decide whether any of it is allowed to ship. Narrowing that far means the second time I see your problem is usually the fifth or sixth time I have seen it.
Essays on agent architecture, plus the shorter notes that usually come first.
Four flows, twenty-six role agents and exactly one human stop per run. The delivery machine my work goes through end to end — how a ticket gets classified, isolated in a sandbox, specified, built, reviewed by a panel that re-runs in full after every repair, and released only once a person says yes.
Most agent test suites pay a model to answer a question the model has no part in: does the next node run, is that gate reachable, does the retry budget actually decrement. Separate transport from judgement and those checks run in seconds, offline, for nothing.
A timeout is not a budget. Budgets that work are enforced on every state write, discount halted time, and hand the decision at the ceiling to something that can read the run — because 'one assertion away' and 'going in circles' both present as a spent budget.
Generating code got cheap; reading it did not. The bottleneck moved from writing to reviewing — so here is the two-tier review agent I run on my own repos: a fast local gate before the commit lands, a deeper agentic pass on the pull request, and the evals that keep both honest.
The work shows up in public before it shows up in a case study.
@buildwithgagan
Build notes, half-finished ideas, and the occasional automation that went sideways.
Follow/in/buildwithgagan
Longer posts on shipping AI systems inside real companies. Where most engagements start.
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Essays and teardowns — the full write-up when a thread is not enough room.
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Open-sourced pieces of the toolkit — harnesses, adapters, and scripts worth stealing.
FollowSend a short note about what you're trying to automate, and I'll reply within a day with a 30-minute call or a fixed-price audit proposal — whichever fits.