01
AI-native products
Greenfield products designed around AI from day 1: copilots, agents, intelligent search, recommendation engines. Production-grade, with eval harness in CI.
AI Software Development
Production AI software — built right the first time.
AISD is an AI-native software development company. We build the full stack — frontend, backend, model integration, retrieval, eval harness, observability — for teams shipping AI to mid-market and enterprise customers. Senior engineers, public pricing, eval harness from day one.
From $45,000 · Eval harness on day 1 · Production runbook on hand-off
What we build
Six surfaces. End-to-end ownership.
02
AI features in existing products
Embed AI capabilities in your existing app — without a full rewrite. Behind a flag, scoped to a beta cohort, measured against real product metrics.
03
AI agents
Autonomous workflows for support, document processing, sales, ops. LangGraph, Pydantic AI, n8n — picked for the workload, not for novelty.
04
Data + retrieval architecture
RAG pipelines, vector + structured retrieval, freshness controls, citation enforcement. The plumbing that makes AI features reliable.
05
Eval + observability
Golden test sets, model-graded scoring, drift monitoring, per-call cost and latency observability. CI integration on every PR.
06
Hardening + deployment
Prompt-injection defense, cost caps, structured outputs, circuit breakers. Production runbook and 30-day post-launch support window.
Stack
Mainstream, pragmatic, AI-native.
Languages + frameworks
TypeScript / Python · React / Next.js / Astro · Node.js / FastAPI · Pydantic AI · Vercel AI SDK · LangGraph
Models
Anthropic Claude · OpenAI · Google Gemini · open-weight Llama / Mistral / Qwen for self-hosted workloads
Data + retrieval
Postgres · pgvector · Pinecone · Weaviate · Supabase · Snowflake / Databricks for analytics-grade pipelines
Orchestration
LangGraph · Pydantic AI · n8n · Inngest · Modal / Replicate for GPU-backed inference
Observability + eval
LangSmith · Langfuse · Datadog · Grafana · Posthog · Mixpanel for product metrics
Infra
AWS · GCP · Azure · Cloudflare · Vercel · on-prem when compliance requires
How we work
Four stages. Real gates between them.
01
Discovery sprint
1–2 weeks. Domain interviews, success metrics, throwaway prototype on the riskiest assumption. Output: a fixed-price proposal.
02
Architecture
Reference architecture matched to your stack. Model selection, retrieval pattern, agent orchestration, eval design — opinionated for production.
03
Build
Senior engineers, weekly demos, eval harness from day 1. AI-paired engineering for 2–4× delivery velocity on greenfield work.
04
Ship + harden
Production deployment with observability, prompt-injection defense, cost caps, runbook. 30-day post-launch support window included.
Related services
Industries we ship
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 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.
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.
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.
How is AISD different from a typical software development agency?
Three differences. First, every AISD engineer is senior — minimum 5 years building production software, with shipped AI features. Second, we publish hourly engagement bands and project ranges so you know roughly what an engagement costs before the first call. Third, we take fewer concurrent projects so a partner stays close to delivery.