Lead Lifecycle Integrity Engine
Real-time pipeline observability from form capture through routing, enrichment, and assignment. Every lead instrumented. Silent leakage surfaces in minutes not at the end-of-month review.
Real Estate & PropTech AI Engineering · Residential · Commercial · Property Management
Real estate Data Is Just Genuinely Hostile.
500+ MLS schemas in residential. Non-standard leases in commercial. Lead pipelines that silently lose 1–8% of revenue before a rep ever sees a contact. This is not a model problem. It is an infrastructure problem. Most real estate tech teams discover that about six months after they ship.
We have been in that room at residential brokerage platforms, commercial real estate SaaS companies, and PropTech scale-ups. The specifics are always different. The pattern is the same: good engineers, data they did not fully account for, and AI that behaves nothing like it did in the test environment. We have seen what happens next, and it does not have to go that way.
What We Build
We build the data normalization layer, the lead pipeline observability, the document validation architecture, and the attribution infrastructure. Real estate AI without that surrounding engineering is a demo that collapses when production data arrives.
Real-time pipeline observability from form capture through routing, enrichment, and assignment. Every lead instrumented. Silent leakage surfaces in minutes not at the end-of-month review.
Sub-5-minute response orchestration with AI-driven lead scoring, intelligent routing, and personalized first-touch outreach. The 21× qualification drop is an engineering problem with an engineering solution.
Normalized pipelines across 500+ MLS schemas. Schema mapping, update cadence management, field normalization, and exception alerting so your AI trains on clean listing data.
Clause extraction across non-standard formats: NNN variations, ROFO provisions, co-tenancy clauses, CAM reconciliations. Structured output validation makes every value traceable to its source passage.
Multi-touch attribution connecting lead source to closed transaction — not just to form fill. Budget decisions backed by data, not gut feel.
Deduplication and entity resolution across MLS feeds, CRM records, property databases, and enrichment sources. One canonical record routing logic that does not break on dirty data.
Automated follow-up sequencing that fires correctly even when lead records are incomplete. SLA enforcement, re-engagement triggers, and intelligent task prioritization.
AI-assisted transaction coordination, automated compliance checklists, maintenance request triage, investor reporting, and natural language property search built on your data architecture, not a generic template.
Logiciel engineers embed directly into your workflow. Sprint planning, architecture reviews, code reviews, standups. We work inside your product architecture, not alongside it.
One sprint to understand your financial data model, architecture, and target use case. Fixed-scope estimate delivered at the end.
We design the validation layer, retrieval architecture, and data grounding before writing a line of production code.
Our team joins your sprints. Code reviews, standup, paired architecture. First production feature in 8 to 12 weeks.
Full documentation, tested infrastructure, and your team fully equipped to maintain and extend what we built together.
The difference between the products winning and the products churning is not which LLM they chose. It is the engineering surrounding it.
Shipped AI features that are reliably correct before scaling them
Built the validation layer, not just the model
Grounded retrieval in real customer financial data structures
Expert users find the AI useful, not alarming
Winning competitive deals on AI reliability
Shipped impressive demos that failed when a real accountant tested edge cases
Repairing customer trust after a bad AI-generated number
Rebuilding the AI layer under pressure with the wrong architecture
Losing deals to competitors with more reliable AI outputs
12-month in-house hiring cycles while the market moves
"98% of CFOs have invested in digitization. Only 41% report that even 25% of their processes are actually automated." (FP&A Trends 2025 Benchmarks)
Every option has tradeoffs. Here is an honest view.
| What you need | In-house ML team | Logiciel |
|---|---|---|
| Real estate AI domain knowledge (residential and commercial) | 12-18 month hiring cycle, domain expertise not guaranteed | Production experience across MLS integrations, commercial document AI, lead pipelines, and property data normalization |
| Lead pipeline observability and leakage detection | Your team learns the problem while building the solution | Full lifecycle audit framework deployed in the discovery sprint |
| MLS normalization and document AI validation | Ongoing maintenance burden as regional schemas change | Unified data architecture covering residential feeds & commercial documents with shared identity resolution |
| Time to first production feature | 12-18 months to productivity | 8-12 weeks |
| Annual cost | $750K–$1.5M per year | Fixed-scope. Starts at $55K. |
| Product delivery accountability | Full ownership over time | Architecture, implementation, QA, and handoff |
Your first production AI feature is live. Your CFO's Q3 deadline is met. Your engineers understand the architecture they are inheriting.
Customers are using the AI features in real planning cycles. Accountants are checking the outputs & trusting what they find. Churn conversations stop starting with AI reliability.
You are in the group of FP&A products that shipped AI that actually works in production. That is the group that wins the next wave of deals.
Both, and property management too. The data problems are different — residential teams deal with MLS fragmentation and lead pipeline failures, commercial teams deal with document AI and lease data complexity, property management deals with both plus tenant records and maintenance data across multiple formats. We have built all three, and we know where the problems look similar and where they genuinely differ.
Almost always a distribution problem. For residential, the training data usually came from one or two MLS boards and did not account for schema variations of others. For commercial, the lease documents in the test set were more standardized than the ones in production. We run a failure mode analysis, identify where model confidence is high but accuracy is low, then rebuild the affected pipeline with broader data coverage and structured output validation.