AI / ML Integrations
Production AI — OpenAI workflows, integrated directly into your existing operations with evaluation, guardrails and observability.
3.1%
Forecast MAPE
120+
Anomalies / week
18
Production models
Capabilities
What you get
- OpenAI agents with tool-use and structured output
- Forecasting and anomaly detection
- Evaluation harnesses and prompt versioning
Engineering stack
Battle-tested tech
- OpenAI
- FastAPI
AI Agents · LLMs · Forecasting
Production-grade intelligence, evaluated continuously
Neural orchestration
12-month forecast
Agent · Finance copilot
Sub-second inference
Quantized models, prompt caching
Evals on every prompt
Regression suites + golden sets
Tool-use over OpenAPI
Typed contracts, deterministic guardrails
Institutional Framework
AI Engineering methodology — deterministic AI
Model Discovery & Eval ADRs
Senior AI architect-led discovery capturing retrieval strategies, model selection, and evaluation metrics. Every prompt iteration is versioned.
Eval-driven trunk delivery
Mandatory human-in-the-loop reviews, automated eval pipelines, and progressive rollout via A/B prompt testing.
LLM Observability
Every agent ships with token tracking, cost dashboards, and trace-level observability for retrieval and reasoning steps.
Guardrail gates, not vibes
Hallucination checks, PII filters, and deterministic safety gates are mandatory CI gates for every model release.
Technical Specifications
What runs underneath
AI Agent Architecture — OpenAI orchestration, strict typed tool contracts, optimized queries, evals on every prompt, deterministic guardrails.
Model orchestration
OpenAI with function calling
Retrieval
MongoDB Vector Search
Latency goal
Streaming TTFT < 800ms for LLM
Compute
Batched inference, prompt caching
Security & Scalability
AI Security posture
Prompt Injection Defense
Input sanitization, system-message hardening, and dedicated classifiers to detect adversarial prompts.
Data Privacy & PII
Automated PII masking in retrieval pipelines and dedicated tenant-isolated namespaces.
Inference Protection
Rate-limiting per user, cost-budgets, and circuit breakers for external LLM provider dependencies.
Model Governance
Full lineage of training data, prompt versions, and evaluation results for regulatory compliance.
Delivery Architecture
How it ships — blueprint to production
A production-grade AI agent architecture with robust evaluation and safety guardrails.
Reference architecture
Client edge → API gateway → services → data plane
Cross-cutting · Observability · Security · CI/CD · IaC
Integration touchpoints
LLM Providers
OpenAI
Vector Store
MongoDB
Compute
AWS / Serverless
Observability
Tracing and token metrics
Security
Guardrails and deterministic filters
Delivery
GitHub Actions, Docker, Terraform
Execution timeline
- 01
Week 0–2
Eval Discovery
Senior AI architect captures the ground-truth dataset and evaluation metrics.
- 02
Week 2–6
RAG Foundation
Database setup, retrieval pipeline hardening, and first agent vertical slice.
- 03
Week 6–12
Iterative Refinement
Prompt engineering, optimization, and evaluation-driven iteration cycles.
- 04
Week 12+
Go-live & Guardrails
Safety audit, cost-optimization, runbooks, and production cutover.