AISD is an AI-native software development company that builds production AI for mid-market and enterprise teams. Three core services: AI Modernization (embedding AI into existing products — copilots, intelligent search, predictive analytics), AI Agents (autonomous workflows for support, document processing, sales outreach), and AI Workflow Automation (n8n, Zapier, Make, Clay).

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.

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 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 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 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.

Case study · Recruiting

85% faster screening. $1.8M saved annually.

A major staffing firm deployed an AI-powered resume parsing pipeline to automate candidate screening, skills extraction, and shortlist generation across 500+ recruiters.

Industry: Staffing / Recruiting
Region: North America
Size: 500+ recruiters
Stack: LLM APIs · OCR · n8n · ATS integration
Engagement: 8 weeks build

Results

Data-driven outcomes.

85%

Reduction in resume screening time

3.2x

More candidates reviewed per recruiter

40%

Faster time-to-shortlist

$1.8M

Annual cost savings

The challenge

Manual screening was the bottleneck.

01
Recruiters spent 60% of their day reading resumes. Each open role attracted 200-500 applications. Manual screening meant most resumes got 6 seconds of attention.
02
Resume formats were wildly inconsistent: PDFs, Word docs, LinkedIn exports, plain text emails. Existing ATS keyword matching missed qualified candidates with non-standard formatting.
03
Skills extraction was unreliable. 'Python' in a resume could mean 10 years of ML engineering or a single college course. Context mattered but couldn't be automated with rules.
04
Candidate-role matching was subjective. Two recruiters would shortlist different candidates for the same role. No consistent scoring methodology existed.

The solution

Four layers of recruiting intelligence.

Multi-format resume parser

OCR + LLM extraction handles PDFs, Word, images, and plain text. Extracts structured fields: contact info, work history, education, skills, certifications. Handles messy formatting that rule-based parsers choke on.

Contextual skills assessment

The LLM evaluates skills in context. It distinguishes between 'led a team of 5 Python engineers' and 'completed a Python tutorial.' Each skill gets a proficiency signal based on evidence in the resume.

AI-powered candidate scoring

Each candidate is scored against the specific job requirements. The agent considers required vs. nice-to-have skills, experience level, industry fit, and career trajectory. Scores are explainable: recruiters see why each candidate ranked where they did.

Automated shortlist generation

Top candidates are automatically surfaced with a summary brief: key qualifications, potential concerns, and suggested interview questions. Recruiters review a curated shortlist, not a raw pile.

"Our recruiters went from drowning in resumes to reviewing curated shortlists. They're placing candidates faster and the quality of matches has actually improved."

VP of Talent Operations

A Leading North American Staffing Firm