In-product AI features
Copilots, automation and intelligence shipped inside your product — with quality gates and unit economics.
Industry
AI for SaaS & Technology
Ship AI features faster and run leaner GTM and support orgs with production-ready AI systems.
- 8–12 wksto ship your first AI product feature
- 70%support automation achievable
- 70%+gross margin protected by cost engineering
Capabilities
AI for product and company
Support, product analytics and workflow AI for SaaS and technology companies.
Support automation
Tier-1 resolution and agent assist grounded in your docs and telemetry.
Product analytics copilots
PMs and CS teams query product data in plain language, with governed answers.
Internal workflow AI
Engineering, sales and ops workflows accelerated with agents and copilots.
How we deliver for SaaS
Ship AI your customers pay for
For software companies, AI is both product and operations. We've shipped on both sides — with the margins to prove it.
Product
AI features that survive the roadmap review
From concept to GA: feasibility on your real data, streaming UX, eval-gated releases and cost-per-action modeled against pricing — AI features that retain users and protect margin.
- Feasibility validated on your data before commitment
- Eval gates wired into your release process
- Unit economics engineered against your pricing tiers
Copilot
Draft renewal summary for Acme Corp
3 accounts show churn risk — view analysis
Ask anything…
Scale operations
Support that scales faster than headcount
Product-aware support agents grounded in docs, release notes and diagnostic telemetry resolve tier-1 completely and brief humans on the rest — CSAT holds while volume triples.
- Resolution grounded in docs and product telemetry
- Version-aware answers that match the user's plan
- Escalations arrive with diagnosis attached
Summarize open claims above $50k and draft escalation emails.
query_claims_db
draft_email
✓ policy_check
Found 12 claims. 3 need escalation — drafts are ready for your review.
Human approval requiredReview →
Velocity
An AI-accelerated company, not just product
Codebase-aware engineering assistants, sales research agents, onboarding copilots — the internal leverage that lets a 200-person company operate like 400.
- Engineering copilots grounded in your codebase
- GTM agents for research, outreach and CRM hygiene
- Ops automation across finance and people workflows
// Route work to the right model const result = await ai.run({ task: "contract_review", context: retriever.fetch(doc), guardrails: ["pii", "grounding"], fallback: "gpt-4.1", });
RESTSDKWebhooksMCP
Use cases
Where software companies deploy AI with us
Copilot in your product
In-app assistance that becomes a retention driver.
Support automation
Tier-1 resolved, tier-2 accelerated, docs self-improving.
AI feature acceleration
Roadmap AI items shipped with senior reinforcement.
Churn and expansion signals
Usage patterns turned into CS playbooks.
Sales engineering assist
RFPs, security questionnaires and demos accelerated.
Internal platform
Shared AI infrastructure for every team to build on.
Delivery
Industry-first delivery
Every engagement is grounded in your domain — regulations, operating models, and the use cases that actually move the needle.
- Domain-ready use cases
- Compliance-aware design
- Ops & CX impact
- Reusable industry playbooks
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1
Map the domain
Identify high-ROI workflows and risk boundaries.
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2
Prioritize use cases
Score impact, feasibility and data readiness.
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3
Pilot with guardrails
Ship a controlled production pilot fast.
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4
Scale across ops
Expand to adjacent processes with shared platforms.
We'd burned two quarters trying to ship our copilot. Their team re-architected it, added evals and cost controls, and we hit GA in ten weeks — at margins our CFO signed off on.
Works with your stack
We speak your stack
Modern SaaS stacks — cloud-native, CI/CD-driven, product-led — are home turf.
- Anthropic Claude
- OpenAI
- Next.js
- React
- Node.js
- Python
- Postgres
- Pinecone
- Kubernetes
- AWS
- Stripe
- Segment
FAQ
Common questions
Should we build AI features in-house or with a partner?
Both — your team owns the product; we bring the AI engineering patterns (evals, routing, cost control) that take quarters to learn the hard way. Capability transfer is explicit: your engineers own it fully at handover.
How do we price AI features?
Model cost-per-action first, then choose between bundling, usage tiers or add-on SKUs based on value concentration. We've done this analysis across many SaaS products and bring the benchmarks.
Our support docs are thin. Can support AI still work?
Yes — we mine resolved tickets for institutional knowledge, generate draft docs from them, and the system improves documentation as a byproduct of answering.
What about our AI features' quality risk to the brand?
Eval gates before every release, confidence thresholds in production and staged rollouts — the same discipline you apply to code, extended to AI behavior.
Next step
Ship the AI your roadmap promises
Bring the feature spec or the support queue — we'll bring the engineering that gets it live.