01
Copilots in existing UIs
Embed an LLM-powered assistant into your existing product. Context-aware, scoped to user data, with eval coverage on the actions it can take.
AI Modernization
Embed AI into your existing product — no rip-and-replace.
We integrate AI into your software where it earns its keep. Copilots, intelligent search, predictive analytics, process automation. Your existing system keeps running while we layer capabilities on top — production-grade, with eval coverage from day one.
8–14 wk typical · Zero-downtime migration · From $60,000
What we modernize
Four patterns that pay back fast.
02
Intelligent search
Replace keyword search with hybrid semantic + structured retrieval. Citations, freshness controls, and observability on what users actually search.
03
Predictive analytics
Production ML for forecasting, anomaly detection, and recommendations. We pick the right model — XGBoost when XGBoost wins, LLM when LLM wins.
04
Process automation
Insert AI steps into existing workflows (approvals, routing, document handling) without rewriting the system around them.
Industries we've shipped
Four verticals where AI modernization moves the needle.
Manufacturing
Predictive maintenance, quality-control vision, supply-chain optimization.
E-commerce
Smart inventory, dynamic pricing, AI-powered search and recommendations.
Healthcare
Clinical documentation, scheduling optimization, RCM agents.
Finance
Fraud detection, risk scoring, automated compliance monitoring.
How we work
Four stages. Honest gates between them.
01
Audit
Honest review of your current product. We tell you what works and what's burning money — including the parts of your stack that should NOT get AI.
02
Architecture
AI-enhanced architecture that integrates with your existing systems. No rip-and-replace.
03
Integrate
Senior engineers embed AI capabilities into your codebase. Surgical, not bulldozer. Your existing system keeps running.
04
Operate
Deploy with monitoring, hand off the runbook, and run 30-day post-launch support. Optional managed-AI retainer for ongoing operations.
Featured case study · QA modernization
Reduced regression testing from weeks to hours.
AI-assisted test generation cut test creation time 70% for a SaaS customer. Same engineers; new tooling; new throughput.
Read the full case study →Outcome
70%
test creation time saved
Industries we modernize
Featured case studies
Related services
Frequently asked
Common questions.
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's the difference between RAG, fine-tuning, and agents?
RAG (retrieval-augmented generation) grounds a model's response in external data — used when answers must be current or proprietary. Fine-tuning changes model weights to teach a specific style or domain — used when prompts can't reliably elicit the behavior. Agents wrap a model with tools and a control loop so it can take multi-step action — used when the task involves decisions and side-effects, not just generation.
How do you ensure AI features are reliable in production?
Five layers: an offline eval harness with golden test sets run on every PR; confidence thresholds and structured-output validation that gate downstream side effects; runtime observability — every model call logged with inputs, outputs, latency, cost; circuit breakers and deterministic fallbacks for every model dependency; and a weekly review ritual where prompt regressions get caught before they become incidents.
How do you handle hallucinations in production AI?
Hallucinations are the wrong mental model — the issue is ungrounded generation. Mitigations applied in layers: ground every factual claim in retrieved sources, returned alongside the answer; structured outputs with schema validation; confidence scoring with thresholds — low-confidence answers are escalated, not surfaced; human-in-the-loop checkpoints for high-stakes actions; continuous eval against a golden set.
What is AISD's discovery sprint?
A paid 1–2 week engagement to scope a project with rigor before committing to a full build. Outputs: a written scope doc with success metrics, a technical architecture, a 1-week throwaway prototype that proves the riskiest assumption, and a fixed-price quote for the build. Typical price: $8,000–$18,000. Customers who run a discovery sprint with us are 3× more likely to ship on time and budget than customers who skip it.
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.
How are deliverables handed off?
Every engagement ends with a handoff package: production deployment, architecture documentation, eval harness with golden test sets, observability dashboards with documented thresholds, on-call runbook, model upgrade procedure, and a recorded walkthrough. Plus a 30-day post-handoff window for questions and clarifications at no cost.
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.