AI implementation partners for established businesses
Before you hire another accountant, underwriter, paralegal, office admin, or support rep,hire the engineer who multiplies the ones you have.
We’re an AI implementation partner. We find what AI should do in your business, and we build it.
Trusted with live operations in mortgage lending, legal services, and accounting.
Built by engineers from Amazon, Carnegie Mellon, UBC, and USC.
- saved after we replaced one client's enterprise CRM
- $500K/yr
- faster evidence coding at a legal services firm, same team
- 2–3×
- from first conversation to working software in production
- Weeks
saved after we replaced one client's enterprise CRM
faster evidence coding at a legal services firm, same team
from first conversation to working software in production
Where it starts
It usually starts with a question.
A brokerage owner
“Why do I pay so much for my software? And why does adding AI cost even more?”
Because you're renting a system built for someone else, and AI is the vendor's upsell. We built one brokerage a system they own: roughly $500K a year saved, AI included.
See how it played outA legal operations lead
“My staff spend most of their day on repetitive work. Can AI actually take some of it?”
Yes. The repetitive part, not the judgment. At a legal services firm, AI does the first pass on every evidence document, and each person now clears the work of two or three.
See how it played outA head of underwriting
“Everything we know lives in my senior people's heads. What happens when one of them leaves?”
We encode that knowledge into your systems. At a mortgage brokerage, lender criteria and file standards now live in software that prepares every application, and the expertise stays and scales.
See how it played outResults
Six operations we made faster, cheaper, or both.
Recent engagements across mortgage lending, legal services, accounting, and consumer wellness, each told the way we run them: the situation, the work, the result.
National mortgage brokerage
- saved per year in licensing and consulting costs
- ~$500K
- users doubled. Software costs didn't.
- 40 → 80
- the quoted AI add-on price, designed out entirely
- $600/user
saved per year in licensing and consulting costs
users doubled. Software costs didn't.
the quoted AI add-on price, designed out entirely
Replacing an enterprise CRM saved roughly $500K a year
A brokerage was paying Salesforce enterprise prices for a CRM that fought how its people worked, and real AI meant Agentforce licenses on top. We built an AI-native replacement they own outright.
Per-seat licensing + AI add-ons
Cost doubles when the team doubles.
The system they own now
40 → 80 users. The bill barely moved.
Want to hear it from a client directly? References available on request.
The multiplier
The math on your next hire.
Hire another worker
Six salaries. Six people’s units of work.
You add one unit of capacity. The process stays the same, the bottlenecks stay the same, and next year the same conversation happens again.
Embed an engineer who rebuilds the process
Five salaries. Ten to fifteen people’s units of work.
Everyone you already employ gets faster. Every future hire lands in a faster operation. The gain multiplies instead of adding.
When someone paid $60,000 a year becomes two to three times faster at the core of their job, you've added a salary's worth of capacity without paying another salary.
That's the allocation we help you make: one embedded team that raises the output of everyone else, including everyone you hire after us.
Why it works
Advice isn’t the hard part. Adoption is.
Strategy consultancies
Sharp analysis of where AI could help your business. Then a report, a roadmap, and a handshake. Execution is your problem.
Software vendors
A capable product and an onboarding portal. Whether your team's way of working changes is your problem.
Build Launch Iterate
We do the strategy work, build the system, and stay embedded through implementation, until the new way of working is simply how your business runs.
Consider us your AI department.
Every business eventually learned it needed an IT department. AI is that kind of capability now: someone has to put today’s models to work in your operation and keep you ready for what ships tomorrow. It’s the forward-deployed model that companies like Palantir proved, applied at a scale that fits established businesses.
How an engagement works
Build. Launch. Iterate.
Embed & understand
We start inside your operation: the constraints, the compliance, the workarounds people have built. Not how the org chart says the work happens; how it actually happens.
Map the judgment line
Together we decide where AI earns a place and where a person must stay in the loop. Some of the most valuable calls we make are about what not to automate.
Build the system and your data engine
Working software in weeks, on a foundation that gathers your scattered operational data into one clean, governed place. That foundation is what every advance in AI plugs into.
Iterate & compound
We measure against the baseline and keep improving. As models get smarter and compute gets cheaper, your operation picks those gains up without re-platforming or starting over. Everything we build, you own outright: if we ever part ways, the system stays and keeps working. That, not lock-in, is why clients keep us around long after launch.
Human in the loop
Where AI stops and people begin.
We don’t ask how much of your operation can be automated. We ask which decisions you’re comfortable delegating, then build the system around that line.
- AI does the work
- A person checks it
- AI continues
- A person approves
- Shipped, with an audit trail
AI does the repetitive, verifiable work
Reading documents, preparing files, categorizing, drafting, reconciling: work that can be checked, where a mistake is cheap to catch.
People own decisions with consequences
Approvals, exceptions, client judgment, anything with regulatory or financial weight. We design those steps for a person to decide, with AI preparing the ground and never deciding.
Every automated step is auditable
You can always see what the system did and why. Nothing high-stakes approves its own work, and there is no automation without an audit trail.
Running live today
- Accountants review and approve every AI-prepared entry before it posts.
- Underwriters own every lending decision; the system only prepares the file.
- Evidence coders confirm every document; the AI proposes, people decide.
Who we are
Engineers who understand businesses.
We hire a rare kind of engineer: as fluent in operations and process as in software. They can sit with your underwriters, your accountants, your front desk, and understand how the work creates value. We’re deliberately small: senior judgment, hands-on builders, and AI leverage on everything. The people you meet are the people doing the work.
The team
A senior-led team that does the work itself.
We keep the team deliberately small so senior judgment leads every engagement. Each engineer works with a fleet of AI agents that multiplies their output, letting a team of eight deliver what a traditional consultancy needs twenty to match. The people below are the people you will actually work with.
8 people. Vancouver and Toronto. Every engagement runs on the same agent platform.
Jeetesh Rup
Head of Strategy & Operations
Toronto
Lawyer turned strategy consultant turned transformation leader. Spent a decade getting large organizations to actually adopt new technology, from AI-driven marketing at TELUS to $1B+ programs at KPMG.
Senior Manager, TELUS · Led teams of up to 20 data scientists, product owners, and strategists. Real-time customer data plus AI/ML modeling cut churn by $19M a year, 250% over target.
KPMG Transformation Strategy · Restructured a £330M+ transformation program for a UK telecom and redesigned benefits management for a $1.2B Department of National Defence program.
Started as a corporate securities lawyer. That mix of legal, strategy, and data experience is what BLI's regulated clients lean on.
Leads BLI's engineering practice and runs its agent infrastructure, keeping AI coding agents moving work forward around the clock on client systems.
Founded Smart Math BC · A tutoring company he has run since 2019. Learned that explaining things clearly to real people matters as much as the code.
Agent infrastructure · Designed and maintains the systems that let BLI's AI agents operate continuously across client projects.
Engineering
Machine learning engineer with deep experience in credit and lending. His background maps directly onto BLI's mortgage operations work.
Head of Product & Technology, Monsoon CreditTech · Five years applying ML to improve loan outcomes for lenders.
Co-founder & CTO, Cuebo · An AI platform for sales teams.
Mentored data scientists for nearly a decade.
Builds production AI systems: multi-agent architectures, human-in-the-loop workflows, and the evaluation layers that make them trustworthy.
Software Development Engineer, Amazon · Led an initiative tied to a $30MM year-over-year revenue increase.
Senior Developer, Unbounce · Built an anti-spam filtering system that earned the most peer-nominated award in company history.
Leads BLI's forward deployed work, building LLM-powered systems end to end, from data pipeline to interface, inside client operations.
Featured by BCIT · Built an ML system that predicts road-repair risk across Vancouver using 23 open datasets.
Ian Jia
Senior Forward Deployed Engineer
Vancouver
Works on-site inside client operations, bridging finance and engineering. His dual background lets him read the business logic and write the automation that replaces the manual work behind it.
Forward deployed · Embeds directly in client teams, translating business processes into working systems.
Works on-site inside client operations.
Founded Clira · Built and launched his own product.
We hire one or two engineers a year. If this sounds like how you want to build, write to hello@buildlaunchiterate.ca.
Tell us what you’re seeing in your business.
It usually sounds like one of these:
- A team at capacity, where the obvious fix is another hire.
- Software bills that grow faster than the business does.
- Revenue you suspect you're leaving on the table.
- Data scattered across systems that AI should be using by now.
- Something new you want to start the right way, with ROI measured from day one.
In one call, we’ll tell you straight where AI will help and where it won’t. References available on request.