Case study · Technology & IT
Private LLM deployment for a Fortune 500 retailer
A national specialty retailer wanted customer-service automation in-house on a private LLM rather than a third-party API. ApTask deployed a six-person team under a 90-day Statement of Work; the system went live in 87 days, automated 65% of Tier-1 queries in the first quarter and is estimated to save 2.0M USD a year.
Last updated · ApTask Editorial Team
2.0M USDAnnual savings · 87-day delivery
Software · Cloud · Data · Security
Challenge
What was the challenge?
A national specialty retailer wanted to bring customer-service automation in-house with a private Large Language Model deployment rather than route customer data through a third-party API. They had the infrastructure budget but not the LLM engineering or MLOps depth.
Solution
What did ApTask deliver?
ApTask deployed a six-person team — two LLM engineers, two DevOps specialists, an MLSecOps lead, and a fractional product manager — operating under a 90-day SOW with hard acceptance criteria. The team built a private inference cluster on the client’s VPC, instrumented retrieval-augmented generation against their product knowledge base, and shipped a graduated rollout plan.
Result
What was the result?
The system went live in 87 days, automating 65% of Tier-1 customer queries inside the first quarter. Estimated annual operational savings of 2.0M USD, with average resolution latency dropping from 9 minutes to 22 seconds for automated tickets.
Engagement model
Which engagement model did the private LLM build use?
A Statement of Work under ApTask’s Managed Solutions model: a 90-day SOW with hard acceptance criteria, delivered in 87 days. Managed Solutions means Project-based engagements where ApTask owns the staffing, the delivery, the substitutions, and the acceptance criteria. You sign a Statement of Work for a defined outcome. We deliver it — milestone by milestone.
At a glance
What are the facts of this engagement, and where are they stated?
Every engagement-specific row below restates a sentence from the Challenge, Solution or Result sections above, which are the case study as ApTask publishes it; the vertical row cites the case-studies hub and the service-page row the page that states the engagement model’s terms. No figure appears here that is not stated there, and the client stays anonymised exactly as the source anonymises it.
| Item | Detail | Stated in |
|---|---|---|
| Client | A national specialty retailer | Challenge, above |
| Vertical | Technology & IT — software, cloud, data, security | Case studies hub |
| Team | Six people: two LLM engineers, two DevOps specialists, an MLSecOps lead and a fractional product manager | Solution, above |
| Engagement model | A 90-day Statement of Work with hard acceptance criteria | Solution, above |
| Service page | Managed Solutions (SOW): project-based engagements where ApTask owns the staffing, the delivery, the substitutions and the acceptance criteria | Managed Solutions |
| Timeline | Live in 87 days | Result, above |
| Outcomes | 65% of Tier-1 customer queries automated in the first quarter; estimated 2.0M USD annual operational savings; resolution latency 9 minutes → 22 seconds | Result, above |
Technology & IT
Why does ApTask staff technology & it this way?
Our deepest vertical. We source from a network of over 2.1 million technology professionals across full-stack engineering, distributed systems, cloud platforms, and applied AI. Every candidate we submit has been screened by a recruiter who knows the stack — the frameworks, the tooling, the trade-offs. If a résumé claims deep experience in something the candidate can’t discuss in a technical conversation, we don’t put them in front of you.
How it works
How does a Statement of Work engagement run at ApTask?
Five steps, as the Managed Solutions page describes them: a paid two-week discovery sprint that produces the SOW itself, a named team drawn from the authenticated network, work shipped in 2–4 week milestones with hard acceptance criteria, transparent operations on the team’s own board, and final acceptance with knowledge transfer. Payment ties to milestone acceptance, not to time-and-materials drift.
- 01
Paid discovery sprint
A two-week scoping engagement that produces the SOW itself: deliverables, milestones, acceptance criteria, communication cadence, and escalation paths. If you don’t proceed after discovery, the SOW is yours to take elsewhere.
- 02
Named team composition
The delivery team is drawn from our authenticated network: technical lead, contributors, QA, project manager, and executive sponsor. You see the named roster and hours before signing.
- 03
Milestone cadence
Work ships in 2–4 week milestones with hard acceptance criteria. Payment ties to milestone acceptance, not to time-and-materials drift, so you can pause, accelerate, or descope at any boundary.
- 04
Transparent operations
You get the team’s Jira or Linear board, the same standups your internal teams run, and a weekly executive dashboard — nothing offshored to a black box, everything auditable in real time.
- 05
Acceptance & knowledge transfer
Final acceptance against the SOW criteria, a structured knowledge-transfer to your internal team, and a post-engagement support window. We optimize for a clean handoff, not the next renewal.
Technology & IT case study, the questions we hear most.
Who was the client in the private LLM case study?
A national specialty retailer on the Fortune 500 list. ApTask does not name case-study clients on the site; reference customers are available under NDA, and the full attribution can be shared during the strategist call.
How long did the private LLM deployment take?
87 days against a 90-day Statement of Work with hard acceptance criteria. The system automated 65% of Tier-1 customer queries inside the first quarter, and average resolution latency for automated tickets dropped from 9 minutes to 22 seconds.
What did the six-person LLM team consist of?
Two LLM engineers, two DevOps specialists, an MLSecOps lead and a fractional product manager. They built a private inference cluster on the client’s VPC, instrumented retrieval-augmented generation against the product knowledge base and shipped a graduated rollout plan.
Which ApTask service covers a private LLM build like this?
Managed Solutions: project-based Statement of Work engagements where ApTask owns the staffing, the delivery, the substitutions and the acceptance criteria. The AI and machine learning talent page covers the individual LLM, MLOps and data engineering roles.
Related pages.
AI and machine learning talent
Applied AI engineers, LLM and MLOps specialists, and the private-LLM managed-solution model.
Managed Solutions (SOW)
Fixed scope, fixed price, milestone-based acceptance; ApTask carries delivery and substitution risk.
Retail, manufacturing and logistics IT staffing
E-commerce platform developers, supply-chain data engineers, SAP and ERP consultants.
Cloud engineers
AWS, Azure and GCP engineers for private-cloud and VPC infrastructure.
Case study: smart-factory transformation
Another fixed-SOW delivery team, commissioned 18 days ahead of schedule.
IT staffing in Austin
Enterprise technology employers hiring cloud, data and software engineers.
Glossary: Statement of Work
What an SOW engagement is, how it is priced and when it beats seats-filled staffing.
All case studies
Six verticals, each with a measured engagement result.
Building AI in-house without the bench to do it?
Tell us the outcome, the data constraints and the deadline. We will scope a Statement of Work with acceptance criteria and a team that has shipped production LLM systems.