Real Estate

Real Estate AI Software That Prices What the Market Pays

Real estate AI software that helps brokerages, proptech teams and investors value faster, qualify leads earlier, and keep listing data current, with models built on your own transaction history.

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45+ Specialists

Compliance-Aware Delivery

99.9% Uptime Targets

7+ Projects Delivered

Listing Data That Goes Stale

A feed updates nightly, an agent edits in the CRM, and the portal shows a third version, so nobody is certain which price is current. We reconcile the sources into one record with a clear precedence order, and surface conflicts rather than silently picking a winner.

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Valuations Nobody Can Defend

A number arrives from a model with no comparable set behind it, so the agent cannot explain it to a vendor and quietly ignores it. We return the range, the comparables it came from, and the adjustments applied, so the conversation is about the reasoning rather than the output.

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Leads Qualified Too Late

Enquiries sit in a shared inbox until someone works down the list, and the ones with real intent have already called a competitor. We score on arrival against your own conversion history and route the ones worth a call immediately, with the reasoning shown.

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MLS and CRM Out of Sync

Two systems hold overlapping truth about the same property, and reconciliation happens when someone notices a discrepancy. We build the sync as an explicit, monitored pipeline with a defined system of record, so drift is caught by an alert rather than a complaint.

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Property Documents Locked in PDFs

Leases, surveys, title documents, and disclosures hold the facts that matter, and every one of them is a scan somebody has to read. We extract the terms into structured fields with the source page attached, so the document becomes queryable without losing the audit trail.

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Fair Housing and Compliance Exposure

Generated listing copy and automated targeting can produce language and outcomes that create real regulatory exposure. We put the constraints into the system as filters and review gates, and test for them deliberately rather than trusting the model to have learned the rules.

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Seasonal Demand Swings

Volume, pricing, and time on market move with the season, and a model trained on an annual average is wrong in both directions. We model seasonality against your own market rather than a national index, and re-fit on a schedule you control.

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Agent Tools Nobody Opens

We put the output where agents already work: the CRM, the listing screen, the phone. Using it is never a separate decision.

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Valuation ranges scored against your own transaction history, returned with the comparables and adjustments that produced them.

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Defensible comparable sets built from parcel, zoning, and transaction data, with the selection criteria shown rather than assumed.

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Descriptions and marketing copy drafted from verified property attributes, filtered against fair housing constraints before a human approves.

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Enquiries scored on fit and intent against your own conversion history, routed on arrival with the contributing factors visible.

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Terms, dates, options, and obligations extracted from leases and title documents into structured fields with the source page attached.

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Natural-language search over your own inventory that understands intent, budget, and constraints, grounded in live listing data.

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Absorption, pricing, and yield analysis over your own portfolio and market, refreshed on the cadence you set.

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Automatic room labelling, feature detection, and condition scoring from listing photography, so inventory is searchable on what it actually shows.

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Business Process Automation

We automate the repeatable parts of a process, from document intake to decisions and handoffs, with an exception path designed before the happy path.

Process discovery
Document intake
Workflow orchestration
Decision automation
Exception handling

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AI Consulting

A short discovery that identifies high-impact use cases, reviews your data and systems, and returns a phased roadmap with costs and risks attached.

Use-case audit
Data readiness
Phased roadmap
Cost & risk model

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AI Integration

We wire AI into the systems you already run, with identity, data, latency and failure behaviour designed before anything ships.

API orchestration
Identity & permissions
Latency budgets
Fallback behaviour
Cost controls

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Chatbot Development

Assistants that answer from your own content, hand off cleanly to a person, and log every conversation for review.

Retrieval grounding
Human handoff
Multi-channel
Conversation analytics
Escalation rules

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Generative AI Consulting

A short engagement that finds where generative AI pays for itself in your business, and where it does not.

Opportunity mapping
Risk & policy review
Pilot design
Cost modelling

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Generative AI Development

We design and integrate large language model features into your product, with retrieval, evaluation harnesses, and prompt governance in place before launch.

Retrieval pipeline
Eval harness
Prompt governance
Cost controls
Guardrails

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We build for brokerages, proptech, and investors regularly, so we arrive knowing what a transaction lifecycle looks like and where a model earns its place. That means fewer discovery cycles spent explaining your own pipeline back to us.

Every valuation and score comes back with the inputs, the comparables, and the adjustments that produced it. An agent who can explain the number uses it; one who cannot ignores it, and we design for the first case.

We fit on your own closed transactions and your own market, not a national average that flattens exactly the variation you trade on. Your data stays yours and is not used to train anyone else's model.

Fair housing and advertising constraints are implemented as filters and review gates, and tested deliberately before launch. We document what the system will not generate, so your compliance reviewer has something concrete to sign off.

Scores, valuations, and drafts appear in the system your agents already have open, not in a separate tool that needs its own login. Your CRM stays the system of record and every output is written back to it.

Markets move, so we agree the re-fit cadence up front and monitor for drift between runs. You see when a model was last trained and on what, rather than trusting a number of unknown age.

You get the approach, the timeline, and the number before anything is built, and change is a conversation rather than an invoice. Engagement models are fixed-scope, retained, or embedded, whichever fits how you run projects.

The same engineers who built it monitor accuracy, re-fit as the market moves, and keep the data pipelines healthy. You get documented systems and a named team that remembers why each choice was made.

A developer at their desk, two screens of code

AI Based Real Estate Property

AI-Driven Property Insights Platform

Landwise NWA transforms commercial real estate workflows by providing AI-powered insights and data integration for faster decisions.

Faster call evaluation

90%

Coaching accuracy

Rep engagement

AI Automation

AI-Powered Content Automation Platform

An AI content engine that turns sales and strategy calls into ready-to-post content, matched to the brand's own voice and scheduled straight to LinkedIn.

30%

Cost reduction

50%

Faster decisions

90%

Prediction accuracy

Change Management

AI-Powered Enterprise Change Management for Large-Scale Organizations

Enterprise change management software letting organisations execute, monitor and sustain change faster, with the progress of every initiative visible in one place.

80%

Reduction in consulting costs

Faster change execution

Higher adoption rates

Construction Management

Real-Time Construction Coordination, From HQ to Field

A construction coordination platform for multi site work, replacing spreadsheets and scattered email with tasks, schedules and on site progress synced in real time.

2 Hrs

Time saved daily

30%

Fewer errors

Reporting speed

06 Process

Our Real Estate AI Software Process

A real estate build runs in several steps, from requirements and roadmap through model design, integration and listing systems, testing and validation, to deployment & retraining.

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Portfolio & Data Discovery

We start with the data you actually hold: listings, closed transactions, CRM history, and whatever sits in documents. Our team assesses coverage, quality, and licensing constraints, then agrees what is realistic to model. That prevents a project built on data that turns out not to exist.
Valuation Strategy & Roadmap

We translate priorities into a phased plan with accuracy targets, timelines, and review controls. The plan covers which decisions the model supports, which it must never make alone, and what the compliance position is for each output.
Model & Comparable Set Build

We build the pipelines and models, then evaluate against transactions you have already closed rather than a held-out public set. Pass thresholds are agreed per property type, and we iterate with weekly demos on real addresses.
CRM & Listing Feed Integration

We integrate with your CRM, listing feeds, and portals through their APIs, with a defined system of record and monitored reconciliation. Integrations are staged and reversible, so live inventory is never at risk during rollout.
Accuracy & Compliance Testing

We test valuation error, comparable quality, and generated content against fair housing and advertising constraints before launch. Results are documented for your compliance reviewer, and the content filters are tested deliberately rather than assumed.
Agent Rollout

We launch with one team or one market, with monitoring and a rollback path held open. Agents see the numbers and the reasoning behind them, and we watch adoption as closely as accuracy before expanding.
Refresh & Model Upkeep

We monitor drift, re-fit on the cadence agreed, and keep the data pipelines healthy as feeds and schemas change. You get documented systems and a named team that remembers why each modelling decision was made.

OpenAI
Claude
Gemini
Llama 3
Mistral
Falcon

LangChain
LlamaIndex
Haystack
DSPy

LoRA
PEFT
Axolotl
Unsloth

Pinecone
Weaviate
pgvector
Qdrant
Chroma

OpenAI API
Anthropic API
Vertex AI
Bedrock

LangSmith
Langfuse
Arize
Helicone

Guardrails AI
NeMo Guardrails
Presidio

Streamlit
Chainlit
Next.js
Vercel AI SDK

08 FAQ

FAQs

Quick answers to common real estate AI software questions before you start your build

Real estate AI software is accurate to the extent that it is trained on the market you actually trade in. We build valuation models on your own transaction history and measure them against your closed deals before launch, so accuracy is a number you have seen rather than a claim.

In most cases yes. We ingest RETS, RESO Web API, and custom feeds, and we work within the licensing terms your MLS sets on data use and display. Where terms restrict processing or retention, we design the pipeline to those limits rather than around them.

Accuracy depends on your data coverage and the property types involved, so we measure it on transactions you have already closed and report the error distribution before you commit. You get a defensible range with the comparables attached, not a single number without reasoning.

Constraints are implemented as filters and review gates in the system rather than left to the model, and we test for prohibited language and outcomes deliberately before launch. We document exactly what the system will not generate so your compliance reviewer can sign it off.

Usually your closed transaction history, current listing data, and CRM activity are enough to begin. We assess coverage and quality during discovery and tell you honestly if a use case is not supportable yet, rather than building on data that cannot carry it.

09 Book a Call

Ready to Build with KoderTal's AI Experts?

Tell us what you are trying to automate or ship. We reply within one business day with a next step and the engineer who would run the work.

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Real Estate AI Software That Prices What the Market Pays