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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.

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
→
6 wk
Faster than vendor RFP · from our work
A build-vs-buy call that saved a fintech from a $400k platform contract
→
7 mo
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
11 wk
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
→
5 wk
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
→
9 wk
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 →

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