LLM Application Engineering_
>> LLM features are product software: prompt and tool contracts, tenant boundaries, retries, fallbacks, and cost caps. We engineer the application layer so AI ships inside real SaaS and enterprise systems.
Calls/month
Production AI volume across live systems
PII detection
Compliance-ready detection accuracy in production AI
Projects
Shipped since 2012 with senior engineers only
Direct answer
LLM features die in production when tenancy, cost, and failure modes are bolted on later. We engineer application boundaries first: scoped retrieval, kill switches, and PII controls where regulated paths demand them.
Trusted by leaders
>> System map
Adjacent AI layers we ship with this expertise. Technology-first, not vendor theater.
>> When this system fits
Honest fit gates. We will tell you when another approach is better.
Strong fit
- SaaS products adding AI features inside existing tenancy and billing
- Enterprise apps that need auditability and PII controls
- Teams burned by demos that collapsed under real traffic or cost
Weak fit
- Prompt-only experiments with no product owner
- Model research projects without an application boundary
Stack: LLM apps · OpenAI · Anthropic · Tools · Guardrails · TypeScript · Python
What we deliver
_> Capabilities on this stack
Application boundaries
Clear ownership between UI, API, model calls, tools, and data classes.
Tenancy and auth
Retrieval and tools scoped to the right tenant and role every time.
Cost and latency budgets
Caching, model routing, and kill switches before invoices surprise finance.
Failure modes
Timeouts, partial tools, graceful degradation, and user-visible recovery.
LLM app delivery
Feature and risk framing
Define the user job, data classes, and what must never leak across tenants.
Production slice
Ship one path with evals, logging, and cost meters.
Scale controls
Add routing, caching, and incident playbooks.
Related projects
_> See how we've applied our expertise
>>Related guides
_> Cite-worthy depth behind this stack
Frameworks and scorecards buyers and answer engines can quote. Each guide links back to delivery proof.
Integrating LLMs into SaaS Boilerplates: Prompts, Tools, Guardrails
A practical guide to add LLM features to a SaaS boilerplate: prompt management, tool calling, and guardrails. Includes patterns, pitfalls, and metrics to track.
LLM Security Playbook: OWASP Ready Controls for Production Apps
A practical LLM security playbook mapped to OWASP LLM Top 10: prompt injection mitigation, safe RAG, tool sandboxing, red teaming, and go live evidence.
How to Choose a Production AI and RAG Partner
Hire a production AI partner when RAG or agents must survive security review and real traffic. Score vendors on shipped systems, compliance, senior delivery, and evaluation harnesses you keep.
Agent Eval, Cost, and CI Gates
Build offline eval suites, online scorers, cost budgets, and merge-blocking CI gates for production agents.
FAQ
No. We design for the providers you approve and keep tool/prompt contracts portable where it matters.
Detection, redaction, and policy gates where the product requires them. We have shipped production AI with >98% PII detection accuracy.
>> Where this goes next
Adjacent expertise and the engagement models we deliver it through.