In-product copilot
+15–35%
feature engagement
Context-aware assistant scoped to user data and product domain. Surface relevant docs, draft outputs, take light actions. Eval-harness validated.
SaaS
AI features that actually move SaaS metrics.
AISD builds AI inside your existing product — copilots, intelligent search, onboarding agents, summarization, power-user workflows. Eval harness from day one. Staged rollout. Measurable impact on engagement, retention, and conversion.
6 proven patterns · 8–12 wk typical build · Frontier APIs by default
Use cases
Six features that change SaaS metrics.
Intelligent search
+25–60%
search-to-action rate
Replace keyword search with hybrid semantic + structured retrieval. Citations, freshness controls, and observability on what users actually search.
Onboarding agent
30–50% ↓
time-to-first-value
Conversational onboarding that learns the user's goal and walks them to it — without forcing them through a static product tour.
Summarization at edges
Daily
exec digests
Long docs, thread digests, change summaries. Embed at the right surface: dashboards, notifications, weekly emails.
Power-user agentic workflows
5–10×
throughput on bulk tasks
Multi-step tasks the user describes in natural language. Bulk operations, schema-aware data manipulation, repeatable playbooks.
AI-powered support
25–40%
auto-resolution
Resolve common how-to and account questions in-app, before they become tickets. Hand off cleanly with full context when escalation is needed.
How we ship AI in SaaS without breaking the app
Three phases. Real measurement gates between them.
01
Pick one feature
Highest-ROI patterns: in-product copilot, intelligent search, onboarding agent. Pick one with measurable user engagement impact.
02
Ship behind a flag
Deploy to a beta cohort. Eval harness validates offline; staged rollout (1% → 10% → 50% → 100%) measures cost, latency, and outcomes.
03
Measure + iterate
Cohort retention, engagement, conversion. Roll back if any metric goes the wrong way. Compound from there.
Frontier APIs vs self-hosted
Default to frontier. Self-host only with evidence.
Most SaaS workloads — copilots, search, summarization, agents — are better served by frontier APIs (Claude, GPT, Gemini) with prompt caching and model routing than by self-hosted open-weight models. Frontier wins on reasoning quality, tool-use reliability, and continuous model improvement.
Self-hosting wins when you have evidence: very high volume (per-call cost outweighs operational overhead), strict latency requirements, or data sovereignty requirements that block cloud-API usage. We size each engagement to the right architecture; no opinion-based default.
Featured case study
QA pipeline: regression testing 3 weeks → 4 hours.
A SaaS customer was burning weeks per release on manual regression. We built an AI-augmented QA pipeline — test generation, E2E automation, performance baked into CI.
Read the full case study →Outcome
98%
regression cycle reduction
Services for SaaS
SaaS-aligned case studies
Frequently asked
Common questions.
What AI features should a SaaS company ship first?
Highest-ROI patterns: in-product copilot (context-aware help, scoped to user's data), intelligent search (replace keyword with hybrid retrieval and citations), AI-powered onboarding (reduce time-to-first-value), summarization at edges (long docs, thread digests, change summaries), agentic workflows for power users (multi-step tasks the user describes in natural language). Pick one with measurable user-engagement impact, ship behind a feature flag, measure via cohort retention and engagement.
How do you build AI features without breaking the existing app?
Layered approach. Add the AI capability behind a feature flag, scoped to a beta cohort. Deploy the model behind a service boundary with cost caps and rate limits. Validate via offline eval first (golden test set on representative inputs), then online metrics. Roll out percentile by percentile — 1%, 10%, 50%, 100% — watching for cost, latency, satisfaction. Roll back if any metric goes the wrong way. Standard staged-deployment hygiene applied to AI.
Should we use frontier APIs or self-host an open model?
Default to frontier APIs (Claude, GPT, Gemini) until you have evidence to justify the operational cost of self-hosting. Frontier wins on reasoning, tool use, and continuous improvement; self-hosted wins on per-call cost at very high volume, latency control, and data sovereignty. Most SaaS workloads — copilots, search, summarization — are better served by frontier APIs with prompt caching and model routing.
How long does it take to build a production AI agent?
Working prototype: 2 weeks. Production-grade agent (with eval harness, guardrails, observability, and a runbook): 6–10 weeks. The prototype-to-production gap is where most projects fail — the prototype handles the happy path; production has to handle the long tail.
What does it cost to build an AI agent?
A production AI agent at AISD typically costs $40,000–$150,000 depending on complexity. Drivers: number of integrated systems, evaluation rigor required, compliance overhead, and ongoing operational scope. Prototypes alone are cheaper ($10k–$25k) but rarely worth it without a path to production.
How long does it take to build an AI MVP?
Most AI MVPs at AISD ship a usable version in 4–8 weeks. Week 1 is a discovery sprint. Weeks 2–6 are the build, with weekly demos and a working version by week 4. Weeks 7–8 harden, document, and hand off.
What does an AI MVP cost?
AISD AI MVPs typically range $45,000–$120,000 depending on scope. Drivers: number of model integrations, complexity of retrieval/data layer, custom UI surface area, and compliance requirements. We publish indicative bands on the pricing page so buyers can budget before the first call.
How does pricing work — fixed-price, T&M, or retainer?
All three. Fixed-price for AI MVPs and agent builds where scope is well-defined after a discovery sprint. Time-and-materials for staff augmentation, billed monthly with a not-to-exceed ceiling. Retainer for ongoing optimization, eval-harness operations, and managed AI services — flat monthly fee for a defined scope of capacity.