Gen-AI and Agentic-AI solutions, each scoped for the realities of a B2B SaaS company — and mapped to the three outcomes that move the business: more top-line, lower cost, or dramatically less time. What used to take days now takes minutes.
Straight talk. These are the solutions we build, with the outcomes we engineer for and ranges drawn from public industry benchmarks. They aren't past client case studies — we're building our public reference book now. When we ship a client outcome, the measured number replaces the range on the relevant card. Honest framing, no borrowed proof.
Every solution below is one of three shapes — Gen-AI, Agentic, or a hybrid of the two — and every one is judged by whether it moves revenue, moves cost, or moves the clock. If it can't be measured on the P&L or the calendar, we don't build it.
Drafting, summarizing, classifying, retrieving. Single-turn or short-turn jobs where a model produces an artifact a human reviews. Lower risk, fast to ship, easy to measure.
Multi-step workflows where the AI plans, calls tools, and acts across systems — with humans approving the things that matter. Higher value, harder to build, demands real evals and guardrails.
Most production systems. A retrieval-grounded model embedded in a multi-step process — e.g. a sales agent that researches, drafts, and queues, with humans approving the sends.
Solutions whose job is to grow revenue — acquire faster, expand harder, retain longer. Measured in pipeline created, expansion ARR, and gross retention.
An agentic system that researches a prospect across public signals, drafts a tailored opener tied to a real trigger, and queues it for human send. Replaces generic blasts with at-scale personalization.
A Gen-AI classifier that reads inbound leads, marketing-qualified signals, and product-usage telemetry — then routes the hottest accounts to AEs in real time with a one-paragraph "why now."
A retrieval-grounded copilot inside your app that answers the new user's actual question — "how do I set up X?" — and walks them to activation. Reduces drop-off in the days that decide LTV.
An agent that monitors usage, support sentiment, and renewal timing — flags at-risk accounts to CSMs with a recommended play and the receipts (quotes, drops, tickets) that justify it.
Semantic, citation-backed search that lets users find the answer in your data or docs in a sentence — not a hunt. A feature you can ship to charge for, not just an internal tool.
Reads product telemetry to find accounts hitting plan limits or using features that signal a higher tier — pushes a CSM-ready upgrade prompt with the data attached. Turns usage into ARR.
Solutions whose job is to cut the cost of running the SaaS — fewer hours per ticket, fewer hours per release, fewer hours hunting through documents. Measured in headcount you don't have to hire and engineering time you get back.
Our wedge. An agent-assist copilot grounded in your help docs and tickets — drafts replies for human approval and confidently deflects repetitive tickets. Eval-first, so the deflection number is real.
A retrieval-grounded reviewer that compares branches, repos, or vendor handoffs — flags drifts, dead code, and risky deltas with citations. For acquihires, vendor diligence, and PR review at scale.
Ingest two (or twenty) policy, MSA, or DPA documents and produce a clause-by-clause diff with risk-flagged deltas. The job that eats legal and ops days.
Turn your wikis, Slack history, decks, and SOPs into a citation-backed answer engine. The new-hire ramp accelerator and the everyone-stop-asking-me-in-DMs tool.
Multi-step agents for the unloved-but-expensive workflows — invoice reconciliation, vendor onboarding, renewal admin. Approval-gated where it matters, autonomous where it doesn't.
A senior AI capability you don't have to hire — embedded across the above use cases, accountable on the same outcome metrics, at a fraction of a hire's cost. The retainer that ties everything together.
Solutions whose job is to compress turnaround time — the operational workflows that quietly stall deals, releases, and renewals. Measured in cycle time: what took days now takes minutes.
Drafts answers to security questionnaires and RFPs from your policies, past responses, and docs — cited, and flagged for SME review. The thing that stalls an enterprise deal for a week.
When something breaks, it correlates logs, traces, and recent deploys, drafts the incident timeline and root-cause analysis, and writes the status-page note — while you're still finding the runbook.
Maps a new customer's data to your schema, generates the config, and runs a validated migration — with humans approving the edge cases. The onboarding that ties up your best engineers for weeks.
Assembles the quote, applies your approval and discount rules, generates the order form, and hands finance a clean invoice. The deal-desk back-and-forth that adds days to every close.
Continuously collects SOC 2 / ISO evidence, maps it to controls, and flags the gaps before the auditor does — so audit prep stops being a quarterly fire drill.
Turns a plain-English question into governed SQL, a number, and a chart — no ticket, no analyst queue. The data request that used to sit in a backlog for days.
The free AI Opportunity Teardown ranks every one of these against your specific operation — what to build first, what to skip, and rough ROI on each. You leave with a plan whether or not we ever work together.
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