How fast can you ship an MVP?

21 calendar days from kickoff to production deploy on the 21-Day MVP path. Custom Software builds run 6 to 26 weeks depending on scope.

How much does an MVP cost?

21-Day MVP from $5,000 USD. Custom Software Development from $11,000. AI Development from $7,000. Final quote depends on scope and integration count.

What stack do you use?

Default stack: Next.js, TypeScript, Node.js, Python, PostgreSQL, AWS, Stripe, Clerk, OpenAI, Anthropic Claude.

What services does Parallel Loop offer?

Three service lines: 21-Day MVP Development (from $5,000), Custom Software Development (from $11,000), and AI Development and AI Agents (from $7,000). Within these, 11 specialised service pillars including mobile app development, ecommerce, automation, CRM integration, cloud DevOps, and enterprise solutions.

How is the 21-Day MVP different from Custom Software?

The 21-Day MVP is productised with fixed scope, fixed price, and fixed 21-day timeline. It fits single-workflow products with off-the-shelf integrations. Custom Software is bespoke, sprint-based, and runs 6 to 26 weeks for products with multiple workflows, regulated scope, or enterprise integrations.

What stacks do you support?

Default stack: Next.js, TypeScript, Node.js, Python, PostgreSQL, AWS, Stripe, Clerk, OpenAI, Anthropic Claude. We can work in Java, .NET, Go, Ruby, PHP, Swift, Kotlin where the existing landscape requires it.

Do you offer AI development separately?

Yes. AI Development and AI Agents is a separate service line covering chatbots, generative AI, machine learning, computer vision, NLP, and AI consultation. From $7,000 for a chatbot MVP up to $35,000+ for a custom AI platform.

What is included in pricing?

Working software, full source code, CI/CD pipeline, staging plus production environments, runbook, monitoring setup, dual on-call cover for 2 weeks post-launch. We do not charge separately for architecture, design, or QA. They are part of every engagement.

Can you handle compliance and security?

Yes. We build HIPAA, PCI DSS, SOC 2 Type II, GDPR, ISO 27001, NERC CIP, IEC 62443 architecture where the build requires it. We do not issue audit certificates. We build the architecture and evidence collection that supports your audit firm.

Do you offer maintenance after launch?

Yes. Maintenance retainer from $2,200 per month. On-call cover, dependency upgrades, small features, security patching. SLA-backed. We do not require a retainer to do a build; we offer it as an option.

How fast can you start?

Typically 2 to 4 weeks from scoping call to kickoff. We do not double-book the team.

What is the average engagement length?

21-Day MVP: 21 calendar days. Custom Software: 6 to 26 weeks. AI Development: 4 to 20 weeks. Maintenance retainers run month to month.

How do I know which service line fits?

Book the free 45-minute scoping call. 30 minutes on your build, 15 minutes on which path fits. We tell you at the call if your build does not fit our service lines.

What is the difference between AI-powered software and an AI feature?

AI-powered software has AI as core to the value proposition. The product would not exist without the AI. An AI feature is a smaller addition to a non-AI product, like adding AI search to an existing app. Both require eval harnesses and guardrails, but AI-powered software needs more substantial AI infrastructure investment.

Do you build on OpenAI, Anthropic, or open-source models?

All three. OpenAI GPT-4o for general-purpose production. Anthropic Claude Sonnet for long-context and safety-sensitive workloads. Open-source (Llama 3, Mistral) self-hosted for cost-sensitive or data-residency-locked workloads via Replicate, Modal, or self-hosted vLLM.

How do you prevent hallucinations?

RAG grounding on factual claims, with source citation. Confidence scoring. Output validation against expected schema. Human-in-the-loop for high-stakes outputs. Hallucination monitoring in production via output sampling. Zero hallucination is not honest engineering, but we measure and reduce.

Can you handle PII and compliance for AI workloads?

Yes. PII redaction on input. Data residency control via Azure OpenAI Service or AWS Bedrock with BAA. GDPR compliance. HIPAA where the build is healthcare. SOC 2 Type II architecture for SaaS controls. EU AI Act risk classification at scoping for EU-facing builds.

Do you build with eval harnesses?

Yes. Every AI workflow ships with an eval set authored at discovery. Pass-fail criteria. CI runs eval set on every PR. Production monitoring includes hallucination rate via output sampling. We do not ship AI to production without eval gates.

Can you fine-tune custom models?

Yes. OpenAI fine-tuning on GPT-4o-mini. Anthropic via partner. Open-source fine-tuning on Llama 3 or Mistral via QLoRA. We recommend fine-tuning only when prompting has hit its ceiling because fine-tuning carries ongoing data and re-training cost.

Do you support multi-step AI agents?

Yes. Multi-step agents with tool definitions, confirmation flow on side-effectful actions, and full audit log. Built with LangChain or custom orchestration. We do not deploy autonomous agents without human-in-the-loop for high-stakes decisions.

How fast is the AI inference?

Depends on model and use case. GPT-4o-mini at sub-1-second for short prompts. GPT-4o at 1-3 seconds. Claude Sonnet at 1-3 seconds. Streaming responses for low perceived latency. Real-time use cases (under 200 ms) typically need fine-tuned smaller models or traditional ML rather than LLMs.

What about ongoing AI model migrations?

OpenAI and Anthropic ship new model versions every 3-6 months. Maintenance retainer from $2,200/month covers model migration: eval set runs against new model, performance compared, prompts adjusted, production cutover. Without active maintenance, AI-powered software degrades as models change.

AI-Powered Software

AI-Powered Software Development AI core to the product, not bolted on.

Custom software with AI features baked in from the start. RAG over your data, AI agents, generative content, ML predictions, and AI-assisted workflows. Production-ready with eval harness, guardrails, and human-in-the-loop where needed. Shipped in 10 to 22 weeks. USD pricing.

Book a free 30-minute AI-powered software scoping call →See how we work ↗

We tell you which AI features fit your build and which would be theatre.

10–22WEEKS TO SHIP

$7K+EMBEDDED AI

EvalHARNESS BUILT-IN

EU AIACT-READY

Get started in 60 seconds

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How we work on AI-powered software

What we build: AI-powered SaaS · Custom AI workflows · AI-assisted tools · Embedded AI in existing software
AI stack: OpenAI GPT-4o · Anthropic Claude · Llama 3 · Mistral · LangChain · LlamaIndex · pgvector · Pinecone
Guardrails: Eval harness · prompt versioning · hallucination monitoring · citation · confidence scoring · human-in-the-loop
Stack: Next.js · Python (FastAPI) · PostgreSQL · AWS · Stripe · MLflow · LangSmith
Pricing in USD: Embedded AI from $7,000 · AI-powered MVP from $11,000 · Production AI-powered software from $21,000 · Custom AI platform from $55,000
Output: Production software · AI features · eval harness · monitoring · runbook · on-call coverage

AI-powered software is different from software with an AI feature bolted on. The architecture, data model, evaluation harness, and monitoring all change when AI is core to the value proposition. This page covers what we build, the patterns we use, and what production AI-powered software actually costs.

Recent AI-powered software builds

Production AI baked into the core product — not a side feature.

GetlemAI context compliance platform

RAG plus deterministic guardrails for a compliance-sensitive workflow.

Read case study →

MedipyxisAI hospital platform

Healthcare AI workflow under regulated controls. GxP-aligned audit trail.

Read case study →

Full library on the case studies page →

What we build

AI-powered SaaS

SaaS products where AI is the primary value. Examples: AI content tools, AI code assistants, AI legal review, AI customer service platforms.

AI-assisted internal workflows

Internal tools that use AI to automate part of a workflow (document review, content drafting, decision support). Human-in-the-loop by default.

AI features in existing software

Embed AI into existing applications. Common patterns: AI search, smart summaries, generative content blocks, AI recommendations.

AI agents and copilots

Multi-step agents that take actions (book appointments, run queries, open tickets). Audit log of every action. Confirmation flow on side-effectful actions.

Custom ML predictions

Lead scoring, churn prediction, fraud detection, recommendation engines. Real-time API. Drift monitoring. Shadow deploys.

Use cases with cost ranges

AI-powered SaaS MVP

Standalone AI-powered product (e.g., AI content tool, AI legal assistant, AI sales tool) with auth, billing, RAG over your data, and an eval harness. Stack: Next.js + Python FastAPI + PostgreSQL + OpenAI or Claude. Typical build 10 to 14 weeks. Range $11,000 to $38,000 depending on workflow count.

AI-assisted internal workflow

Embed AI into an existing internal workflow (document review, content drafting, decision support). Human-in-the-loop. Audit log of every AI suggestion and human decision. Typical build 8 to 12 weeks. Range $11,000 to $21,000 depending on integration count.

AI features embedded in existing software

Add AI search, AI summarisation, generative content, or AI recommendations to your existing product. Eval harness and guardrails wired in. Typical build 6 to 10 weeks. Range $7,000 to $21,000 depending on feature count.

Multi-step AI agent platform

Agent that takes actions (book, query, open ticket). Tool definitions. Confirmation flow on side-effectful actions. Audit log. Typical build 12 to 18 weeks. Range $35,000 to $55,000 depending on tool count and audit complexity.

How we run the build

Five-phase rhythm for AI-powered software builds. Eval harness authored before any production code is written.

1Weeks 1–2Discovery and AI scope

2Weeks 2–4Architecture and prompt design

3Weeks 5–16Build

4Weeks 17–18UAT and safety review

5Week 19+Launch and dual on-call

Discovery and AI scope (1 to 2 weeks). AI use case definition. Quality criteria. Eval set authored. Compliance scope (EU AI Act, NIST AI RMF). Output: eval set plus AI scope document.
Architecture and prompt design (1 to 2 weeks). Model selection per task. Prompt iteration against eval set. RAG architecture if applicable. Output: ADRs plus prompt library.
Build (5 to 12 weeks). Two-week sprints. Eval gate on every PR. Hallucination rate monitored. Cost-per-call tracked.
UAT and safety review (1 to 2 weeks). Real-user testing. Prompt injection red-team. PII handling validated. Output sampling for hallucination rate.
Launch and dual on-call (1 week plus 2 weeks). Production deploy with monitoring on AI quality. Output sampling. Model version migration runbook delivered.

Tech stack

Default AI-powered software stack. Each layer chosen for production reliability, traceability, and ongoing model governance.

LLM layer. OpenAI GPT-4o for general-purpose. Anthropic Claude Sonnet for long-context and safety-sensitive workloads. Llama 3 or Mistral self-hosted for cost-sensitive or data-residency-locked workloads.
RAG layer. LangChain or LlamaIndex for orchestration. pgvector, Pinecone, Weaviate for vector storage. Hybrid search (BM25 + semantic) by default. Re-ranking with Cohere or cross-encoder.
ML layer. scikit-learn, LightGBM, XGBoost for tabular. PyTorch for deep learning. MLflow for model registry. Feast or Tecton for feature store.
Application layer. Next.js (TypeScript) for front end. Python (FastAPI) for AI services. Node.js (Fastify) for general API. Stateless services behind API gateway.
Data layer. PostgreSQL with pgvector for embeddings. Redis for cache and rate limiting. S3 for document storage. Snowflake or BigQuery for analytics warehouse.
Evaluation. Custom eval harness per workflow. LLM-as-judge for subjective tasks. Automated eval set runs on every PR via CI. Hallucination monitoring in production via output sampling.
Safety. Source citation. Confidence scoring. Human-in-the-loop where regulators or your brand require. PII redaction. Prompt-injection defence.

PII, compliance, and AI workloads

Every AI-powered build ships with PII redaction on input, data residency control via Azure OpenAI Service or AWS Bedrock where required, GDPR compliance by default, HIPAA architecture where the build is healthcare, and SOC 2 Type II-ready SaaS controls. EU AI Act risk classification at scoping for EU-facing builds. We do not ship AI to production without eval gates.

Pricing

Embedded AI features

From $7,000

Add AI to existing product (search, summary, recommendations).
6 to 10 weeks.

AI-powered MVP

From $11,000

Single AI workflow, RAG or LLM-based, eval harness baseline.
10 to 14 weeks.

Production AI-powered software

From $21,000

Multi-workflow with RAG, eval harness, hallucination monitoring, citation.
12 to 18 weeks.

AI agent platform

From $35,000

Multi-tool agent with audit log and confirmation flow.
14 to 20 weeks.

Custom AI platform

From $55,000

Multi-model, multi-workflow, fine-tuning or LoRA, full eval suite.
16 to 22 weeks.

Maintenance retainer

From $2,200 / mo

On-call cover, prompt updates, eval set expansion, model migration, hallucination monitoring.

Pricing shown above is indicative. Exact scope and fee confirmed on the scoping call. Expect $200–$3,250/month in LLM API spend on top of build cost, depending on traffic.

FAQ

AI-powered software is custom software where AI is core to the value proposition rather than a side feature. The architecture, evaluation harness, monitoring, and human-in-the-loop design all reflect AI's central role. Examples include AI content tools, AI legal assistants, AI customer service platforms, and AI-assisted decision support systems.

Ready to scope your AI-powered software build?

Book a free 30-minute AI-powered software scoping call →