Embed AI into SaaS · Chicago, IL

Ship AI features inside your Chicago vertical SaaS.

Relativity already shipped aiR. Morningstar shipped Mo, its research-grade assistant. Sprout Social ships AI assist for social copy. Enova uses model-driven underwriting. Outcome Health, the new GrubHub stack, and the next wave of project44 competitors are all in the same race. Vertical SaaS in Chicago is the AI front line right now, and your customers know it.

You do not need a separate AI product. You need AI inside the product you already sell. The customer who logs into your e-discovery dashboard, your policy-admin system, or your shipment-visibility platform should find AI features in the same screens they use today.

Pricing for an AI-embedding engagement is scoped per feature, based on what you need built.

How we work

Tell us which AI feature your customers are asking for.

What is actually happening in Chicago vertical SaaS.

Relativity launched aiR for Review and aiR for Privilege inside the existing Relativity One product. That move reset the expectation for every other e-discovery and legal-ops platform in the market. Customers now ask, on every demo, "where is your version of aiR." Saying "we are evaluating" is no longer an answer. They want a feature in the product.

In insurance technology, the pressure comes from a different direction. Brokers and underwriters are pushing carriers and policy-admin SaaS vendors to add automated policy-language extraction, AM Best report summarization, and AI-assisted claims triage. Carriers who used to wait for IT roadmaps are now buying point solutions that bolt onto your platform if you do not ship the feature.

Trading and post-trade platforms in the Loop (Tradeweb, Cboe-adjacent SaaS, derivatives analytics) need natural-language interfaces over market data, automated explanation of large P&L moves, and surveillance pattern summarization. The regulatory backdrop is FINRA, CFTC, and SEC, which means every AI output that touches a trading or compliance workflow needs a reproducible audit trail.

Supply-chain SaaS (project44 competitors, freight-tech, customs platforms) is racing to ship AI exception handling: a model reads carrier messages, classifies the disruption, suggests the next action, and drafts the customer notification. Done right, this collapses a 20-minute manual workflow into a 30-second approval. Done wrong, it ships the wrong ETA to a Fortune 100 shipper and ends the contract.

What we build inside your existing vertical SaaS.

AI architecture inside your existing stack

We add an AI services layer to your .NET, Java, or Python codebase with a single gateway for every model call. Logging, caching, rate limiting, and tenant isolation enforced in one place, not sprinkled through controllers.

LLM integration with your domain data

Retrieval connected to your policy database, your matter management system, your shipment-event stream. Citations point to a specific row, a specific clause, a specific event. No hallucinated data, because the model is shown the source.

AI UI inside your existing screens

An assistant sidebar on the matter detail page, an AI command palette in the policy editor, an exception-handling panel in the shipment view. We use your design system, your component library, your interaction patterns.

Evaluation harness with real production prompts

Golden dataset of 200 to 500 prompts from your domain (e-discovery review decisions, policy-clause extractions, shipment classifications) with expected outputs. Regression tests on every prompt change before merge.

Model versioning with audit trail

Every model call records the model ID, prompt version, retrieval source, output, and reviewer override. When an exam or audit asks which model handled a 2025 claim summary, the answer is one query away.

Cost guardrails per tenant and per feature

Per-tenant monthly budgets, per-user token caps, prompt caching with Anthropic, batch API for nightly summarization, tiered model routing. We instrument cost so your CFO can price the AI tier accurately.

Chicago vertical SaaS, by industry.

Four industries dominate the Chicago vertical SaaS map. Each has a different bar for what "ship-ready AI" means.

Legal-tech and e-discovery

Relativity set the bar. Document review with AI assist, privilege detection, predictive coding refined with LLM prompts, deposition summarization. Every output needs a citation back to the source document, page, and paragraph. Reviewer overrides must be logged. Chain of custody cannot break.

InsurTech and policy-admin SaaS

Policy-language extraction, AM Best and rating-agency report summarization, claims-narrative triage, automated coverage analysis. The model touches financial decisions, so every output has to be traceable to its source clause and reviewable by an underwriter. SR 11-7-style model risk governance is now table stakes even at non-bank carriers.

FinTech, trading, and analytics

Morningstar-style research summarization, Cboe-adjacent derivatives analytics, post-trade compliance surveillance, payments analytics. FINRA, CFTC, and SEC backdrop. Every AI output that touches a trading rationale, a research note, or a compliance flag has to be reproducible years later.

Supply-chain and freight-tech

project44, FourKites-adjacent visibility platforms, customs and trade-compliance SaaS, freight-marketplace operations. AI exception handling, customs-hold classification, automated customer notifications, predictive ETA refinement. The cost of a wrong AI output is a misrouted shipment to a Fortune 100 customer.

Questions from Chicago vertical SaaS teams.

Our e-discovery SaaS runs on .NET and SQL Server. Can you embed AI features without us migrating to a Python stack?: Yes. We do not require a Python migration. We add a thin AI services boundary (typically C# wrapping the Azure OpenAI SDK or a small Python sidecar for libraries like LlamaIndex where C# coverage is incomplete) and keep the rest of the .NET monolith untouched. Model calls go through an internal gateway that your team can monitor with the same Application Insights or Datadog setup you already use. For Chicago legal-tech specifically, this matters because the chain of custody story stays intact: prompts, retrieved documents, model outputs, and reviewer overrides all log into your existing audit tables, not a parallel system.
How do you handle source citations for an AI feature inside an insurance policy-management product?: Every model output that summarizes or extracts from a policy carries citations back to the source: document ID, page, section, and the verbatim quote that supported the summary. The UI shows the citation as a click-through pill next to the generated text. Internally we run a verification step that re-checks each cited claim against the source passage before the model output is shown to the user. If verification fails, the system flags the output as unverified and routes it to a reviewer queue. For AM Best summarization, claims-handling notes, and policy-language extraction at carriers, this is the only architecture that survives an underwriting audit.
What does pricing look like for adding AI to a Chicago supply-chain SaaS product?: Pricing is scoped to the feature, not quoted off a rate card. A targeted feature like AI-assisted exception handling on shipment delays (LLM classifying the root cause from carrier messages, suggesting a remediation, surfacing the right contact) is a smaller build than a broader one. A broader build (in-product chat over your shipment data, automated tagging of customs holds, generated customer notifications with approval workflow) is a larger scope with a correspondingly larger engagement. We instrument cost per feature per tenant so you can decide whether to bill it as an add-on or roll it into the base price. Most of our Chicago clients end up pricing it as a premium tier.
We need to test new models against our production prompts before switching. What does that look like?: We build a golden dataset of 200 to 500 prompts pulled from your real production traffic (PII scrubbed) with expected outputs reviewed by a domain expert on your team. The dataset lives in your repo. Every prompt change or model swap runs through Promptfoo or Braintrust against this dataset and emits a regression report: accuracy, latency, cost per call, and citation correctness for retrieval-augmented features. For legal-tech and insurance customers, we also run an adversarial set covering ambiguous policy language and edge-case claim narratives, because those are the prompts that fail silently in production.

Ready to ship the AI feature your renewals depend on?

Tell us your stack, the feature your customers keep asking for, and the audit edge you have to clear. We reply within one business day with a rough scope and price.

Ship AI features inside your Chicago vertical SaaS.

Pricing for an AI-embedding engagement is scoped per feature, based on what you need built.

What is actually happening in Chicago vertical SaaS.

What we build inside your existing vertical SaaS.

AI architecture inside your existing stack

LLM integration with your domain data

AI UI inside your existing screens

Evaluation harness with real production prompts

Model versioning with audit trail

Cost guardrails per tenant and per feature

Chicago vertical SaaS, by industry.

Four industries dominate the Chicago vertical SaaS map. Each has a different bar for what "ship-ready AI" means.

Legal-tech and e-discovery

InsurTech and policy-admin SaaS

FinTech, trading, and analytics

Supply-chain and freight-tech

Questions from Chicago vertical SaaS teams.

Our e-discovery SaaS runs on .NET and SQL Server. Can you embed AI features without us migrating to a Python stack?

Yes. We do not require a Python migration. We add a thin AI services boundary (typically C# wrapping the Azure OpenAI SDK or a small Python sidecar for libraries like LlamaIndex where C# coverage is incomplete) and keep the rest of the .NET monolith untouched. Model calls go through an internal gateway that your team can monitor with the same Application Insights or Datadog setup you already use. For Chicago legal-tech specifically, this matters because the chain of custody story stays intact: prompts, retrieved documents, model outputs, and reviewer overrides all log into your existing audit tables, not a parallel system.

How do you handle source citations for an AI feature inside an insurance policy-management product?

Every model output that summarizes or extracts from a policy carries citations back to the source: document ID, page, section, and the verbatim quote that supported the summary. The UI shows the citation as a click-through pill next to the generated text. Internally we run a verification step that re-checks each cited claim against the source passage before the model output is shown to the user. If verification fails, the system flags the output as unverified and routes it to a reviewer queue. For AM Best summarization, claims-handling notes, and policy-language extraction at carriers, this is the only architecture that survives an underwriting audit.

What does pricing look like for adding AI to a Chicago supply-chain SaaS product?

Pricing is scoped to the feature, not quoted off a rate card. A targeted feature like AI-assisted exception handling on shipment delays (LLM classifying the root cause from carrier messages, suggesting a remediation, surfacing the right contact) is a smaller build than a broader one. A broader build (in-product chat over your shipment data, automated tagging of customs holds, generated customer notifications with approval workflow) is a larger scope with a correspondingly larger engagement. We instrument cost per feature per tenant so you can decide whether to bill it as an add-on or roll it into the base price. Most of our Chicago clients end up pricing it as a premium tier.

We need to test new models against our production prompts before switching. What does that look like?

We build a golden dataset of 200 to 500 prompts pulled from your real production traffic (PII scrubbed) with expected outputs reviewed by a domain expert on your team. The dataset lives in your repo. Every prompt change or model swap runs through Promptfoo or Braintrust against this dataset and emits a regression report: accuracy, latency, cost per call, and citation correctness for retrieval-augmented features. For legal-tech and insurance customers, we also run an adversarial set covering ambiguous policy language and edge-case claim narratives, because those are the prompts that fail silently in production.

Ready to ship the AI feature your renewals depend on?

Tell us your stack, the feature your customers keep asking for, and the audit edge you have to clear. We reply within one business day with a rough scope and price.