GreyScript AI
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Applied AI Engineering for
Production Environments

GreyScript AI, the applied-AI division of GreyScript Technologies, designs and builds LLM applications, intelligent agents, and AI infrastructure engineered to meet enterprise standards for reliability, scalability, and measurable business outcomes.

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Claude/GPT/Gemini/Llama/Mistral/DeepSeek/Qwen/Model Context Protocol/Claude Agent SDK/OpenAI Agents SDK/LangGraph/CrewAI/AWS Bedrock/Google Vertex AI/Azure AI Foundry/Databricks/Snowflake/Pinecone/Weaviate/pgvector/PyTorch/Kubernetes

Years of combined AI engineering experience, shipped in production

0
AI systems shipped to production
0 Years
Average team engineering experience
0%
Uptime across active deployments
0 Weeks
Median time to first working prototype

As of September 2026.

End-to-end AI development, from strategy to on-call support

Fourteen disciplines we staff, price, and ship independently, from new web and mobile builds to AI added into what you already have, plus voice, security, and internal company systems. Select one to see exactly what's included.

AI Strategy & Readiness
AI-Native Web & SaaS Apps
AI-Native Mobile App Development
LLM & Agent Applications
MCP & Agent Tooling Development
Custom Model Development
AI Integration Into Existing Products
MLOps & AI Infrastructure
Computer Vision & NLP
Workflow & Company-Wide Automation
Data Engineering & Preparation
Voice & Conversational AI
AI Chatbots & Website Copilots
AI Governance & Security
AI Strategy & Readiness
We help you identify high-value AI use cases, assess whether your data and infrastructure are ready, and build a clear adoption plan before any engineering time is committed.
Use-case discovery and prioritization workshops
Data readiness and infrastructure assessment
Build-vs-buy analysis for your specific use case
ROI modeling and business case documentation
Ethical and compliance guardrail planning
Typical timeline
2–4 weeks
Engagement type
Fixed-fee
Best for
Pre-build clarity
14
Ideas scored · from our work
Fourteen AI ideas, scored down to three worth building
→
6Weeks
Faster than vendor RFP · from our work
A build-vs-buy call that saved a fintech from a $400k platform contract
→
7Months
Payback period · from our work
An ROI model picked the one AI project that paid back in seven months
→ Book a strategy call →
AI-Native App Development
Building a product from scratch where AI is the core of what it does, not a feature bolted on afterward. The architecture, data model, and UX are all designed around the model from day one.
Product architecture designed around model behavior and latency
Full-stack build from zero to launch-ready
Prompt and context architecture as a first-class part of the product
Cost-aware model selection for your usage pattern
Launch support and post-launch iteration
Typical timeline
8–14 weeks
Engagement type
Full build
Best for
Greenfield products
11Weeks
Zero to launch · from our work
A natural-language analytics product, built AI-native from zero
→
42%
Fewer support tickets · from our work
An onboarding copilot cut new-account support tickets by 42%
→
68%
Less drafting time · from our work
An AI-native proposal builder cut RFP drafting time by 68%
→ Book a strategy call →
AI-Native Mobile App Development
Mobile apps built AI-first from the ground up, drawing directly on our team's mobile engineering background. On-device inference where it matters, cloud inference where it doesn't, and a native experience either way.
Native iOS and Android builds, or cross-platform where it fits
On-device model deployment for latency-sensitive features
Cloud-backed AI features with offline-friendly fallbacks
App Store and Play Store-ready release engineering
Battery, latency, and cost profiling for on-device inference
Typical timeline
10–16 weeks
Engagement type
Full build
Best for
iOS & Android launches
76%
Lower cloud cost · from our work
On-device AI cut latency and cost for a fitness app's core feature
→
22%
Lift in repeat purchase · from our work
On-device personalization lifted repeat purchases without sending data off-phone
→
35%
More inspections per shift · from our work
Offline on-device AI let utility inspectors complete 35% more inspections per shift
→ Book a strategy call →
LLM & Agent Applications
Chatbots, copilots, and multi-agent workflows built on production-grade orchestration, tested against real usage patterns rather than shipped as demos.
Conversational product design and prompt architecture
Multi-agent orchestration and tool-use pipelines
Retrieval-augmented generation (RAG) systems
Evaluation harnesses tied to business KPIs
Production deployment with monitoring and guardrails
Typical timeline
6–10 weeks
Engagement type
Full build
Best for
Copilots & chat products
3.5×
Faster triage · from our work
A multi-agent system cut fault triage time by 3.5 times
→
9×
Faster first-pass review · from our work
A contract-review agent cut legal's first-pass read time nine times over
→
64%
Tier-1 resolved end to end · from our work
An IT service desk agent now resolves 64% of tier-1 requests end to end
→ Book a strategy call →
MCP & Agent Tooling Development
Building Model Context Protocol servers and agent tools that give an LLM safe, structured access to your systems: your database, your internal APIs, your file storage, your third-party services.
Custom MCP server design and implementation
Tool and function-calling schemas for your internal systems
Permission and access-scoping for agent-facing tools
Multi-agent tool orchestration and hand-off design
Testing and evaluation of tool-use reliability
Typical timeline
4–8 weeks
Engagement type
Full build
Best for
Agent tool access
14
Tools exposed · from our work
An internal MCP server gave every team's tools to one agent interface
→
5
Tools unified · from our work
One MCP server gave every sales agent the same five CRM tools
→
80%
Fewer ad-hoc data requests · from our work
A governed MCP server cut ad-hoc data requests to the analytics team by 80%
→ Book a strategy call →
Custom Model Development
Fine-tuning, evaluation, and domain adaptation treated as a discipline in its own right, not an afterthought bolted onto a general-purpose model.
Fine-tuning on proprietary or domain-specific data
Evaluation harness design and benchmark tracking
Model selection and cost-performance tradeoff analysis
Prompt engineering as a documented, versioned artifact
Ongoing model performance monitoring post-launch
Typical timeline
6–12 weeks
Engagement type
Full build
Best for
Domain-specific accuracy
94.1%
Classification accuracy · from our work
A fine-tuned model closed an 18-point accuracy gap prompting couldn't
→
61%
Fewer false positives · from our work
A custom fraud model cut false-positive declines by 61% without adding risk
→
23%
Less perishable waste · from our work
A custom demand forecast cut perishable waste by 23% across 38 stores
→ Book a strategy call →
AI Integration for Web & SaaS Products
Adding AI features into your existing web app or SaaS platform without a rewrite, working inside your current codebase, auth model, and release cadence.
API-first integration into existing backends
Feature-flagged rollout with zero downtime
Compatibility audit of current architecture
Incremental migration plans for legacy systems
Post-integration performance and cost review
Typical timeline
3–7 weeks
Engagement type
Incremental
Best for
Existing products
31%
Fewer support tickets · from our work
AI search added to an existing SaaS product without a rewrite
→
5Weeks
Added without a rewrite · from our work
An AI tutor was added to a five-year-old LMS without touching its core
→
3.4×
Faster dispatch · from our work
AI triage added to an existing property platform made work order dispatch 3.4 times faster
→ Book a strategy call →
MLOps & AI Infrastructure
Deployment pipelines, observability, and cost controls that scale with your usage instead of surprising you with a bill at the end of the month.
CI/CD pipelines for model and prompt deployment
Vector database architecture and retrieval tuning
Cost monitoring and token-usage optimization
Latency and drift monitoring with alerting
Infrastructure-as-code for reproducible environments
Typical timeline
4–8 weeks
Engagement type
Ongoing
Best for
Scaling AI at cost
38%
Cost reduction · from our work
Cost and observability overhaul cut AI infrastructure spend 38%
→
47%
Lower inference spend · from our work
Multi-model routing cut a media platform's inference bill by 47%
→
52%
Lower cost per request · from our work
Self-hosted open-weight model serving cut per-request cost by 52%
→ Book a strategy call →
Computer Vision & NLP
Specialized tracks for document processing, image analysis, and text classification, built and evaluated against your actual data distribution.
Document classification and extraction pipelines
Image analysis and object detection systems
Text classification and intent recognition
OCR and structured-data extraction from unstructured sources
Multilingual and domain-specific NLP tuning
Typical timeline
6–10 weeks
Engagement type
Full build
Best for
Documents & images
3.1×
Faster line speed · from our work
A custom vision model ended a fatigue-driven accuracy swing
→
19×
Faster lease abstraction · from our work
NLP extraction turned six-week lease abstraction into two days
→
71%
Fewer false alarms · from our work
Edge vision for site safety cut false alarms by 71% on existing cameras
→ Book a strategy call →
Workflow & Company-Wide Automation
AI-driven automation for internal operations, from a single high-friction process to company-wide workflows, scoped to what actually saves time first, not the whole department at once.
Process mapping and automation opportunity assessment
AI-assisted internal tooling and dashboards
Human-in-the-loop review workflows
Integration with existing ticketing, CRM, and internal systems
Change management and team onboarding support
Typical timeline
2–5 weeks
Engagement type
Fixed-fee
Best for
Internal operations
11 hr
Saved weekly · from our work
One automated process, chosen by data instead of opinion
→
89%
Invoices auto-processed · from our work
An automated pipeline now processes 89% of invoices without a human touch
→
5→1 day
Account opening time · from our work
Automated KYC intake cut member account opening from five days to one
→ Book a strategy call →
Data Engineering & Preparation
The pipeline work that makes every discipline above reliable in production, handled as core engineering rather than a one-time cleanup pass.
ETL and ELT pipeline design and implementation
Data cleaning, labeling, and quality validation
Data lake and warehouse architecture (Snowflake, Databricks)
Schema design for retrieval and fine-tuning workloads
Ongoing data quality monitoring and alerting
Typical timeline
4–8 weeks
Engagement type
Foundational
Best for
RAG & fine-tuning prep
7
Sources unified · from our work
A real data foundation, built before any model work started
→
12 TB/day
Sensor data reliably ingested · from our work
A rebuilt pipeline made twelve terabytes of daily sensor data usable
→
96%
Retrieval accuracy · from our work
Preparing 1.2 million regulated documents made retrieval accurate enough to trust
→ Book a strategy call →
Voice & Conversational AI
Low-latency voice agents for phone lines and in-app voice interfaces, built on streaming pipelines so response time feels like a real conversation, not a delayed one.
Streaming speech-to-text, LLM, and text-to-speech pipeline design
Real-time integration with scheduling, CRM, or order systems
Barge-in support and natural interruption handling
Confidence-based handoff to a human agent
Call recording, transcription, and ongoing QA review
Typical timeline
6–10 weeks
Engagement type
Full build
Best for
Phone & call center volume
73%
Calls handled automatically · from our work
A voice agent that handles most calls without a human
→
31%
Higher average order value · from our work
A voice ordering agent lifted average order value by upselling correctly
→
38%
More booked appointments · from our work
A voice agent booked 38% more service appointments by answering every call
→ Book a strategy call →
AI Chatbots & Website Copilots
Grounded, curated chat widgets that answer real product and pricing questions accurately, and know when to hand off to a human instead of guessing.
Curated, expert-reviewed knowledge base scoped to your actual product
Strict grounding on pricing and capability claims, no open-ended generation
Lead qualification and warm handoff into your sales or support workflow
Native widget integration matched to your site's design
Content-ownership process so answers stay current after launch
Typical timeline
4–6 weeks
Engagement type
Full build
Best for
Pre-sales & support
44%
Of qualified demo requests · from our work
A website copilot answered pre-sales questions a demo used to
→
58%
Tickets deflected · from our work
A support chatbot deflected 58% of tier-1 tickets in its first quarter
→
71%
Lower cost per contact · from our work
An order-aware support chatbot cut cost per contact by 71%
→ Book a strategy call →
AI Governance & Security
Independent adversarial review of AI agents and systems before or after launch, finding the prompt injection, tool-misuse, and data-exposure risks internal QA usually misses.
Prompt injection testing across direct and indirect data channels
Tool-use boundary and authorization-scope testing
Data exfiltration and system-prompt exposure testing
Prioritized findings report with severity ratings
Remediation support and post-fix re-testing
Typical timeline
3–5 weeks
Engagement type
Audit
Best for
Pre-launch review
11
Exploitable findings · from our work
An adversarial security review found what internal QA missed
→
4
Undisclosed model risks found · from our work
An independent audit found four model risks an insurer's own team had missed
→
9Weeks
To audit-ready · from our work
An EU AI Act readiness program got a recruiting platform audit-ready in nine weeks
→ Book a strategy call →

How an engagement actually runs

Five phases. The same team that scopes the work deploys and maintains it, so nothing gets lost in a handoff.

1
Week 1–2
Discovery
We map your data, constraints, and the narrowest use case that proves value fastest, before committing to a full build.
2
Week 3–5
Prototype
A working system built against your real data, evaluated against metrics we agree on together before development begins.
3
Week 6–7
Pilot
The prototype runs against a limited slice of real usage before we commit engineering time to hardening it for everyone.
4
Week 8–11
Production build
The system is hardened, monitored, and integrated into your stack by the same engineers who built the prototype.
5
Ongoing
Operate
We continue monitoring performance, tuning cost, and updating models, staying accountable for how the system runs after launch.

AI built around the problems your industry actually has

We don't apply the same generic playbook everywhere. Each vertical below reflects real constraints we've worked inside of, from regulatory review cycles to mobile-first customer bases.

Fraud detection agents, transaction risk scoring, and personalized financial experiences, built to satisfy audit and explainability requirements from day one.
More on AI for fintech & payments →
Clinical document processing, patient-data analysis pipelines, and workflow automation, engineered around HIPAA-aligned data handling.
More on AI for healthcare & life sciences →
Recommendation engines, demand forecasting, and AI shopping assistants integrated directly into existing mobile commerce apps.
More on AI for retail & eCommerce →
Document classification, route optimization signals, and predictive delivery systems that replace manual triage queues.
More on AI for logistics & supply chain →
AI copilots and in-app assistants added to existing products without a rewrite, drawing on our own mobile engineering background.
More on AI for SaaS & mobile products →
Predictive maintenance signals and quality-control vision systems designed to run reliably on constrained edge hardware.
More on AI for manufacturing & operations →

Work we've shipped

A few of the systems we've shipped into production, with the number that mattered to the client.

Fintech · Customer Support
61%
Ticket deflection
RAG-based support agent cut first-response time in half
Deployed a retrieval-augmented support agent grounded in the client's internal knowledge base, deflecting the majority of L1 tickets without adding headcount, with full audit logging for compliance review.
RAGAgentsFintech
Read the full case study →
Logistics · Operations
4.2×
Throughput
Document pipeline replaced a manual triage queue
Built a classification and routing pipeline that reads incoming shipping documents and routes them in under a second, quadrupling throughput without new hires.
Computer VisionAutomationLogistics
Read the full case study →
Retail · Mobile
6Weeks
Time to ship
In-app shopping copilot, integrated with zero downtime
Added a conversational shopping assistant into an existing GreyScript-built mobile app. Same codebase, same release cadence, no service disruption during rollout.
LLM AppsMobileRetail
Read the full case study →
View all case studies →

What actually makes an engagement work

Applied engineering, not slideware
Our engineers bring practical, production experience to every engagement, designed around real-time inference, retrieval systems, and adaptive pipelines, and tested against business KPIs, not benchmark leaderboards.
Infrastructure that scales with you
We build platforms that grow with your organization: integrated with your existing data estate, supporting streaming and batch workloads, with automated monitoring that catches performance drift before your customers do.
Responsible modeling by default
Data quality drives model performance. We pre-process, reduce bias, and test against domain-specific conditions, with interpretability layers so outputs can be audited and trusted in day-to-day operations.
One team from scoping to on-call
The people who scope your project are the same people who deploy and maintain it after launch. No handoff between a sales team and an engineering team you've never met.

The frameworks behind every system we ship

We're a focused engineering team, not a certified compliance vendor, but every system we design accounts for the standards our clients are typically held to. We'll tell you plainly where a formal audit or certification is your responsibility, not ours.

GDPR
Data protection by design, for systems handling EU user data
HIPAA
Data-handling patterns aligned with covered-entity requirements
SOC 2
Logging and access-control patterns that support your own audit
NIST AI RMF
Risk categorization following the NIST AI Risk Management Framework
XAI
Interpretability layers so model outputs can be explained and audited
EU AI Act
Risk-tiering awareness for systems serving EU markets
CCPA
Consumer data-rights handling for California-facing products
MODEL GOVERNANCE
Versioning, rollback, and lifecycle tracking for every deployed model

Models and infrastructure we work with

We stay model-agnostic and pick the right tool for the job, not the one we're most comfortable with. This spans everything from a mobile AI feature to a full agentic system with MCP-based tool access.

Foundation models
Claude
GPT
Gemini
Llama
Mistral
DeepSeek
Qwen
Amazon Nova
Agents, MCP & orchestration
Model Context Protocol (MCP)
LangChain / LangGraph
Claude Agent SDK
OpenAI Agents SDK
CrewAI
AutoGen
Custom tool-use pipelines
n8n
Retrieval, memory & data
Pinecone
Weaviate
pgvector
Databricks
Snowflake
PostgreSQL
Redis
Elasticsearch
Mobile & on-device AI
Core ML
TensorFlow Lite
ONNX Runtime
React Native
Swift / Kotlin
On-device inference
Edge deployment
Push-based agent triggers
Cloud, MLOps & infrastructure
AWS Bedrock
SageMaker
Google Vertex AI
Azure AI Foundry
PyTorch
TensorFlow
Docker
Kubernetes
Evaluation, observability & automation
LangSmith
Weights & Biases
Custom eval harnesses
Zapier / Make
Temporal
Airflow
Sentry
Datadog

Leadership That Stays Ahead of the Curve

The core leadership behind GreyScript Technologies' enterprise mobile engagements, now driving excellence in applied AI.

Gagan Shetty
Chief Executive Officer

Gagan transitioned from leading enterprise-scale software initiatives within global MNC environments to building a focused, boutique organization designed for strategic growth. He brings an enterprise legacy into a high-performance setting where precision and accountability remain non-negotiable.

His leadership is partnership-first, prioritizing long-term value over short-term wins. Equally central to his vision is a people-centric culture. By cultivating a thriving environment, he ensures the team is empowered to excel because a strong internal culture directly drives client success.

Dhairya Ganatra
Chief Technology Officer

Dhairya represents what modern technical leadership should be. More than a technologist, he acts as the bridge between complex business problems and elegant, scalable solutions. His philosophy is clear: technology must serve the business objective, never overshadow it.

As a mentor and guardian of code quality, he upholds architectural integrity across every project. Under his leadership, every system is engineered to be secure, scalable, and future-proof, not just functional. Leadership at GreyScript is about setting the standards that define how we build, partner, and grow.

What clients say after launch

Real feedback from teams we've shipped production AI systems for.

We've worked with several teams, but GreyScript is the first that actually treats the system as a business driver rather than just a technical exercise. They didn't just hand over a model that worked in a demo; they built a pipeline rooted in how our data actually behaves. Seeing a jump in engagement within a month of rollout proved their approach is as reliable as it is well-engineered.

Julian Voss
CEO, Creative Pulse
Health TechAI-Native Product Engineering

In production AI, a missed deadline is a real cost. What stood out about GreyScript was their transparency throughout the build. They managed the sprints with total predictability, delivering a complex, multi-system integration exactly when they said they would. It's rare to find a team that hits a launch date without compromising reliability in the final week.

Anita Desai
Operations Director, Veridian Tech
FintechMulti-Platform System Integration

Working with GreyScript feels like having an elite in-house team. Their communication is effortless, they bridge the gap between technical complexity and executive-level strategy without any gaps in information. We always knew exactly where the project stood, which made the entire process remarkably stress-free.

Marcus Thorne
CEO, Thorne & Co. Global
Enterprise SaaSApplied AI Engineering

GreyScript has a way of making high-stakes development feel incredibly manageable. We brought them a set of complex integration challenges that had stalled our progress for months, and they dismantled those roadblocks within weeks. They have a rare ability to take a messy, complicated problem and return a clean, working solution without any hand-holding from our side.

Jordan Hayes
VP of Product, Synapse Labs
AI InfrastructureComplex Systems Integration

We weren't looking for another chatbot bolted onto our stack. We needed an AI layer that actually worked across our existing systems and internal knowledge base, with role-based access built in from day one, not added later. GreyScript understood that distinction from the first call.

Ethan Caldwell
Chief Technology Officer, Velsorin Systems
Enterprise SoftwareEnterprise AI Operations Platform

The interesting part of this project was never just adding AI to our manufacturing floor. It was connecting predictive maintenance and anomaly detection to the decisions our operations teams actually make every shift. GreyScript spent real time understanding our processes before writing a line of code, and it shows in how the platform gets used.

Olivia Hartmann
VP, Digital Transformation, Merqovia Industries
Industrial ManufacturingAI Manufacturing Intelligence Platform

We'd experimented with individual AI tools before, but they stayed isolated from how the business actually ran. GreyScript helped us think about AI as an operational system, multiple specialized agents working within controlled workflows, not a collection of disconnected features. That architecture gives us a clear path to expand automation department by department.

Daniel Mercer
Chief Operating Officer, Qorvellan Systems
Business TechnologyMulti-Agent Business Automation

Our challenge was making AI feel native to the product, not a feature bolted onto the side. GreyScript worked directly with our product team to understand the actual user journeys before designing the AI experience around them. That combination of product thinking and AI engineering is rare.

Lauren Mitchell
Head of Product, Talveriq Solutions
SaaS / B2B TechnologyAI-Native SaaS Platform

We started this as an AI strategy exercise. It became clear fast that the real opportunity was much bigger. GreyScript connected the strategic roadmap to actual working technology and workflows, not just a slide deck. Moving from high-level planning into real implementation was the strongest part of the engagement.

Lucas Bennett
Chief Innovation Officer, Arqelvion Group
Professional ServicesEnterprise AI Transformation

Tell us what you're trying to build.

30-minute scoping call. No deck, just questions.

Book a scoping call greyscripttech@gmail.com