01
AI consulting & strategy
Identify high-value use cases, data readiness, and integration paths before you commit to build.
AI development
AI that connects to real business systems.
Custom AI applications, workflow automation, and intelligent integrations — built with the plumbing, governance, and delivery rigor enterprise clients expect.
The real challenge
Most AI projects fail on plumbing, not models.
AI application development is the engineering work that connects models to your data, systems, and review workflows — not a chatbot demo. Clean data, integrations, feedback loops, and operational guardrails matter more than the model headline.
02
Custom AI applications
Production workflows — verification, routing, analytics, and ops — not demo-grade prototypes.
03
ML & predictive systems
Models wired into real business systems with monitoring, feedback loops, and maintainable pipelines.
04
Generative AI & agents
LLM integrations, RAG, and agent workflows with governance, guardrails, and human oversight built in.
Industries we serve
Focused verticals — not a generic AI factory.
We concentrate on engagements where production delivery, compliance, and integration depth matter — agencies, public sector, and SaaS products.
01
Agencies & digital partners
Agency AI delivery is white-label engineering capacity for client briefs that need production AI — not bench experiments.
- Client-facing AI workflows under your brand
- Document verification and ops automation
- MCP and enterprise system connectors
- Senior-led delivery with clear handover
02
Government & public sector
Public-sector AI must run on controlled infrastructure with auditability, bilingual UX, and defensible review queues.
- On-premise and air-gapped model deployment
- High-volume document screening pipelines
- Expert review and appeals workflows
- Citizen-facing mobile-first platforms
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SaaS & product companies
Product AI means models wired into billing, analytics, and customer workflows — not a chatbot bolted on at the end.
- RAG and LLM features in live products
- Analytics and prediction pipelines
- API layers for multi-tenant SaaS
- MLOps, monitoring, and iteration
What we build
End-to-end AI delivery — not slide-deck demos.
Custom AI development covers strategy through production — integrations, governance, and handover included.
- AI integration for existing products
- Proof of concept & MVP development
- Document verification & workflow automation
- Data preparation & pipeline engineering
- MCP and enterprise system connectors
- Ongoing optimization & model iteration
Technology
Production stack — not buzzword bingo.
We choose tools based on your constraints, team, and compliance requirements — not whatever launched last week.
Languages
- Python
- TypeScript
- PHP
Frameworks
- Laravel
- Node.js
- React
- Next.js
AI & ML
- OpenAI
- Claude
- RAG
- LangChain
- MCP
Infrastructure
- AWS
- GCP
- Cloudflare
- CI/CD
Security & compliance
Certified practices. Regulated delivery experience.
Quick Brown Fox maintains ISO/IEC 27001 and SOC 2. On client work we apply controls appropriate to the regulatory environment — including healthcare and EU privacy requirements from builds like Somml Health.
Compliance scope is tailored per engagement; we do not claim blanket certification for every framework on every project.
Organizational certifications
- ISO/IEC 27001Information security management system
- SOC 2Service organization security controls
Regulatory delivery experience
- HIPAA-aligned deliveryHealthcare and virtual-care workflows with privacy, access control, and auditability — including Somml Health.
- GDPR-ready architectureData minimization, consent flows, and privacy-by-design for EU-facing products.
- Audit trails & access controlRole-based permissions, logging, and human-in-the-loop review for high-stakes AI workflows.
Delivery playbook
Prototype fast. Ship with confidence.
A phased AI development process that validates on real data before you commit to full production scope.
Phase 01
Use case & data audit ~2 weeks
Define the business outcome, data sources, compliance constraints, and what success looks like in production.
Phase 02
Prototype & validate 3–4 weeks
Working prototype on real data — enough to test accuracy, latency, and integration risk before full build.
Phase 03
Production build 6–12 weeks
Hardened APIs, observability, fallbacks, and handover docs — AI that survives client scrutiny and scale.
Case studies
Challenge → approach → result.
Production AI across government verification, SaaS analytics, and HIPAA-aligned healthcare delivery.
Government programme via MSL
State heritage digitization
- Challenge
- Citizens needed to submit heritage artefacts at scale, but manual review could not keep up — and AI had to run on government infrastructure without external API dependency.
- Approach
- We built an on-premise AI pre-screening pipeline with duplicate detection, authenticity checks, and expert review queues — English and Hindi, mobile-first.
- Result
- On-premise AI — zero recurring API cost
- 4–5 week delivery on a government timeline
- Full source handover to the client team
B2B SaaS startup
Arkreach PR analytics SaaS
- Challenge
- Arkreach needed an AI-first PR analytics platform that could process massive news data and sell credibly to enterprise agencies.
- Approach
- We engineered the full-stack SaaS — real-time campaign measurement, persona-based media planning, and crisis monitoring in one product.
- Result
- Enterprise-scale AI architecture from scratch
- Article-level insights beyond domain tracking
- Production platform ready for agency clients
Healthcare startup
Somml Health virtual care
- Challenge
- A virtual-care platform had to handle sensitive health data with HIPAA-aligned workflows — not just feature velocity.
- Approach
- We partnered from MVP through production with compliance-aware architecture, access control, and healthcare-specific delivery judgment.
- Result
- HIPAA-aligned workflow design from day one
- Understood virtual-care compliance constraints
- Trusted delivery partner, not a generic dev shop
Discuss healthcare AI“They understood the complexity of virtual care, HIPAA requirements, and healthcare compliance from day one.”
FAQ
Common questions about AI development.
Straight answers on timelines, integrations, white-label delivery, and compliance — without pricing guesswork.
When should we invest in custom AI development instead of off-the-shelf tools?
Custom AI makes sense when your data, workflows, or compliance requirements do not fit a generic SaaS tool — especially for agency client briefs, regulated industries, or products where AI must integrate with existing systems. We start with a use-case and data audit before recommending build vs. buy.
How long does it take to build a production AI application?
A focused prototype typically takes 3–4 weeks. Production-ready systems with integrations, governance, and observability usually land in 6–12 weeks depending on data quality, compliance scope, and integration depth. We scope timelines explicitly before build starts.
Can you integrate AI into our existing product or agency stack?
Yes. Most of our work connects AI to live systems — CRMs, CMS platforms, data warehouses, internal tools, and client environments. We use APIs, event pipelines, and MCP-style connectors so AI augments what you already run.
Do you work with agencies on a white-label basis?
Yes. We regularly deliver as a technical partner behind agency brands — fixed ownership, structured sprints, and handover documentation so your client relationship stays intact.
How do you handle security and compliance?
Quick Brown Fox maintains ISO/IEC 27001 and SOC 2 practices. On engagements we apply controls appropriate to your environment — including HIPAA-aligned healthcare delivery (Somml Health), GDPR-ready data handling, access control, encryption, and audit trails. Compliance scope is defined per project.
What support do you provide after AI goes live?
We offer monitoring setup, model iteration, performance tuning, and clear handover so your team or agency can own the system long-term. Ongoing support is scoped based on whether you need active engineering or periodic optimization.
Work with us
Have an AI initiative that needs production delivery?
From agency briefs to enterprise automation — senior engineers, clear scope, and systems that integrate with what you already run.