We have built inside every stage of the lending lifecycle.
Dealer portals and origination. Credit decisioning and verification. Funding, servicing, collections and the reporting underneath all of it. TechGrit is an AI-first product engineering partner for indirect and dealer-originated lenders who have a platform that works and cannot be paused while it improves.
The constraint is rarely the code.
It is usually that one or two people understand how the platform actually works, the documentation left with a contractor years ago, and every roadmap item queues behind maintenance. These are the conversations we walk into.
Underwriters and credit officers spend their week on manual review that a decisioning layer should be absorbing.
Onboarding a new dealer or merchant takes days of gathering documents from a dozen different places.
Each new lender, bureau or payment integration is scoped from scratch, so the network grows at the speed of the integration backlog.
Fraud losses come from transactions that were funded before anyone confirmed the work was actually done.
Portfolio and compliance reporting is assembled by hand across five or six source systems, and arrives after the month it describes.
The origination or servicing core is twenty years old, undocumented, and nobody wants to be the one who touches it.
Where we work across the lending lifecycle
Dealer-originated and indirect lending across home improvement, water treatment, recreation and marine, plus credit union and bank-side lending platforms. Select a stage.
Dealer and merchant network
The network is the origination engine. Everything downstream inherits the quality of who was approved and how fast they were onboarded, which is why we treat diligence as a speed problem before it is a risk model.
The systems we have already integrated
A lending platform is never one system. It is thirty or forty, connected by contracts nobody wrote down. This is the map, and we did not learn it from a brochure.
AI where it changes the economics, not where it demos well
Every item below carries an honest status. We would rather tell you what is live and what is not than let you find out later.
Credit decisioning
A decisioning engine that sits beside the origination platform and takes on the applications that previously fell to manual review. Classical models carry the scoring, a reasoning layer carries the judgment, and the credit officer's institutional knowledge is captured as the training source rather than guessed at. Built for fair-lending compliance: every decision carries reasons and an adverse-action narrative, and exceptions route to a human. It augments your rules engine rather than replacing your policy. The system of record is untouched.
Prefunding verification agent
A voice agent that calls the end customer before funds move and works through a structured question set confirming identity, transaction terms and whether the work was actually completed. Built for a lender whose fraud losses came from jobs that were never done. SMS and RCS confirmation channels under evaluation.
Dealer due diligence
Underwriting a dealer meant days of gathering documents from a dozen places. We reframed it as a speed and summarization problem first and a decision model second: assemble the packet, summarize it, and put a human in front of a decision rather than a scavenger hunt.
Servicing call summarization
Call transcription and summarization piped straight into account notes with a human in the loop, removing the manual write-up between one call and the next. Agents keep the judgment; the system does the retrieval and the first draft.
Document comparison and extraction
OCR, classification and structured extraction across submitted documents, then comparison of what was submitted against what was agreed. The output is an exception list for a person to work, not a verdict.
Payment operations automation
Robotic process automation on recurring payment operations, removing a manual step that ran every business day and freeing the team that owned it for work that needed judgment.
When the core is the hard part
For credit unions and community institutions, we build the origination platform itself. A loan origination system is a workflow engine wrapped around one hard problem: reading and writing member data in somebody else’s core banking platform. The workflow is well understood. The core integration is not.
The adapter is the product
Every core interaction passes through a single adapter interface, and the application layer never knows which core it is talking to. Swapping Symitar for KeyStone, DNA or an in-house socket changes one implementation and nothing above it. Six operations, defined up front: authenticate, find member, get profile, get financial position, get loan history, and book loan. If a core cannot support all six, that is a scoping finding rather than a surprise at integration time.
Modern core, or institutional core
A modern core has a published, versioned, vendor-maintained contract, and adapter effort is measured in weeks. An institutional core has business rules that live only in code, and adapter effort is a program rather than a task. That distinction decides your timeline, your risk and where the cost lands, which is why we establish it before anyone talks about a date. The first question on a lending engagement is not what stack. It is which core, and does it have an API.
Every field carries its source
Core, bureau, or officer keyed, with the originating account number or pull timestamp. Never inferred, never optional. An officer always knows what came from where, and so does an auditor.
Declared against reported
Per trade line, on one line, with a computed delta. An undeclared trade line is surfaced but never imported silently. The officer decides and the ratios recalculate. A bureau report cannot show this.
Wet signature is not optional
Members will come into a branch and sign on paper. Digital, wet, or a mix within one packet, with routing visible and the archive step explicit, because that is where the retention clock starts.
Cores we build adapters for: Jack Henry Symitar via SymXchange, SilverLake and CIF 20/20 via jXchange, Corelation KeyStone, Fiserv DNA, and in-house institutional cores where the message contract has to be recovered before it can be implemented.
Delivered in lending
Client names withheld where we do not have naming permission. Sector and stage shown instead.
Dealer portal, mobile and origination
Portal, mobile and broker API intake for a dealer network, carrying application submission through to decision handoff, with the integration layer underneath it. Modernization, a vendor transition and standing managed services on one account.
Modernization, transition and managed services in a single relationship
AI decisioning and verification
An AI credit decisioning engine in production alongside the origination platform, plus a prefunding verification voice agent addressing fraud losses on work never performed. Origination and servicing platforms deliberately untouched.
Decisioning engine in production, verification agent in build
Specification recovery on a 30-year platform
A BBx and PRO/5 lending core serving dozens of credit unions, with no documentation and no vendor tooling. No AI model ships training data for that stack, so we built the extraction pipeline ourselves.
Of undocumented business logic recovered into plain-English specification
“TechGrit’s combination of deep enterprise experience and AI-first delivery is rare in the mid-market. They have been doing the hard work for over a decade: complex integrations, modernization, regulatory-grade software. Now they are accelerating it with AI in a way that is practical, not aspirational.”Jonathan M. Gelhaus · Chief Information Officer, Time Investment Company · TechGrit Advisory Board
Nobody should fund a build that has not been scoped
We start with a short, fixed-scope discovery. It produces a plan you can fund, defer, or take somewhere else. There is no obligation to continue with us, which is exactly why it is easy to say yes to.
Prism
Our AI-assisted analysis pipeline, built per technology stack. It reads a codebase and produces specifications, data models, dependency maps and disposition analysis, including on stacks no model was trained on. It informs decisions. It does not make them.
Orbit
Our build platform and AI ecosystem. Governed agents and reusable assets that run inside your process, your repository and your review gates. The custom skills built during an engagement stay with your team.
4D
Discover, Define, Design, Deliver, with a gate at each step. The method decides when a pipeline is used, what a human signs off, and what has to be true before the next phase starts.
We speak the language of the business, not just the stack
A partner who does not know what a stip is, or why a roll rate matters, spends your team’s time getting educated. These are the rules and the scoreboard we already work inside.
The regulations that shape every system
The metrics that decide whether it worked
What lenders ask us first
Will you replace our loan origination or servicing system?+−
Our core platform has no documentation. Can you still work on it?+−
How do you handle compliance and examiner requirements?+−
We are already working with another partner. What does a transition look like?+−
How do you use AI without putting our data or our decisions at risk?+−
We are a credit union. Can you work with our core?+−
What does it cost to get started?+−
Tell us which stage is hurting
Pick whichever part of the lifecycle is closest to something you are working on and we will walk it end to end, in detail, with the engineers who built it. If we are not the right fit, we would rather find that out early than build a case for it.