Four core services covering the full chain — from assessment to rollout, from selection to operations; AI adoption is deployed on site by Forward Deployed Engineers
Managed Digital Steward Service
Outsource day-to-day IT and digital affairs to dedicated stewards while you focus on business; AI adoption is deployed on site by a Forward Deployed Engineer (FDE), accountable for business outcomes.
SLA: Response time and coverage windows are defined by contract, with service tiers customizable on demand.
A dedicated TAM Technical Manager Steward for regular inspection and proactive maintenance
Unified work-order response with fast closure of common issues
Visible operational status so nothing important slips through
AI projects deployed on site by a Forward Deployed Engineer (FDE), accountable for business outcomes
On site, we deploy LLM capabilities into your real production environment and stay accountable for the business outcome — the core delivery model for AI adoption within our Digital Steward service.
A Forward Deployed Engineer (FDE) is an engineer stationed on site who deploys general-purpose LLM capabilities into the client's real production environment and stays accountable for the business outcome. The deliverable is not a slide deck or a demo, but a production-grade system that runs inside legacy systems, dirty data and old permission models — and moves the numbers.
Forward
Not async from headquarters — on site, in the client's meeting room, watching the problem with the frontline team.
Deployed
Already deployed into the client environment — working inside their ticket system, Excel ledgers and legacy data.
Engineer
Ships production code, not design documents. The code must actually run and actually be used by the client.
The five-step FDE workflow
01
Get on site and understand the business
Work shoulder to shoulder with business and IT teams to map the real process, blockers and decision chain — defining the real problem, not the merely plausible-looking one.
02
Define the real problem
Translate business pain into measurable technical problems: which business metric AI must move, rather than piling on features.
03
Build a working prototype fast
Connect data and system interfaces via the shortest viable path; deliver a runnable prototype within two weeks so the business sees real results early.
04
Iterate and validate on site
Validate repeatedly against real environments and real data, aligning latency, accuracy and business metrics until the frontline adopts and keeps using it.
05
Codify into reusable capability
Abstract field experience into industry templates, components and standards, feed them back into the in-house platform to shorten delivery cycles and cost for the next client.
How FDE differs from traditional delivery
Same AI project, completely different deliverable and accountability boundary.
Dimension
Traditional outsourcing / presales / architect
FDE — Forward Deployed Engineer
Working mode
Remote delivery, task-by-task against a requirements list
Embedded on site, iterating at business speed
Deliverable
Documents, demos and solution decks
A running system in production that people actually use
Accountability
Accountable for effort and acceptance sheets
Accountable for business metrics: hours, cost, response time
Data reality
Assumes clean data; integration failure is an IT problem
Spends most effort cleansing fragmented legacy data
Knowledge feedback
Ends at project close; experience stays in project docs
Field experience flows back into product and industry templates, shortening the next project cycle
Typical deployment scenarios
AI Customer Service & Ticket Response
Connect CRM, ERP and ticketing systems: auto-answer repetitive inquiries and route complex ones to the right group.
Lower response time and higher first-contact resolution
Document & Form Intelligence
Automatic extraction, validation and posting of contracts, invoices, statements and other unstructured documents.
Processing cycles compressed from hours to minutes
Production & Quality AI on the Floor
Visual inspection, predictive maintenance and process optimization — requires an FDE on site to bridge edge devices and MES data.
Lower defect rates and fewer unplanned downtimes
Business Data Q&A & Decision Support
Consolidate fragmented operational data into trustworthy, traceable definitions that executives can query directly.
One source of truth and far less time spent extracting numbers
Our FDEs work as one team with three dedicated stewards: the BAM Customer Manager aligns business and acceptance, the TAM Technical Manager secures systems and operations, and the AIM AI Steward provides model and data capability — while the FDE owns on-site delivery. Field experience is codified into industry templates and fed back into our in-house platform, so the next client deploys faster and at lower cost.
One API across major model providers, solving model selection, integration adaptation and compliance governance in one go — so your team focuses on the business instead of trial-and-error across dozens of models.
Pain point 1
Hard to choose models
Dozens of mainstream models each fit different scenarios; identifying the right one through trial and error is costly.
Platform answer
Unified access, side-by-side comparison
One API across major model providers — compare quality and cost on the same interface, and choose by scenario instead of betting on one vendor.
Pain point 2
High integration cost
Every platform has its own API convention, draining development teams; token usage and spend are opaque.
Platform answer
Integrate once, transparent usage
A unified interface cuts adaptation work; the console meters every call in real time with itemized consumption, so spend is fully visible.
Pain point 3
Compliance risk
Overseas APIs require cross-border calls with uncontrollable data-export risk, while domestic compliant models remain fragmented and hard to govern.
Platform answer
Compliant and auditable
Unified permission and audit standards keep in-domain data paths traceable, satisfying internal compliance and external audit requirements.
Team management + transparent billing
Model management
API Key management
Cost management
Log auditing
Multi-tenant & permissions
Master account with tiered sub-accounts
Per-department and per-project model permissions
Independent API keys per account, fully auditable
Usage-based billing, transparent cost
Every call metered in real time by token
Itemized consumption in the console
Budget alerts plus monthly reconciliation
Typical enterprise use cases
Automated customer service
Route intent recognition and response generation across models per business line.
Faster response
Marketing copy at scale
Generate multi-channel copy in bulk; people only review and refine.
Higher content throughput
Business data analysis
Natural-language data extraction and report interpretation, reducing ad-hoc BI requests.
Shorter wait for data
Multilingual product docs
Localize technical docs and manuals in bulk with consistent terminology.
Faster go-to-market docs
One account, two platforms
Rongxin MaaS is a service within AITbuddy, yet runs as an independently deployed platform
Registering an AITbuddy account automatically provisions a MaaS account — no separate sign-up
Jump straight from AITbuddy to the MaaS console; the same account works on both
