AI engineering and platforms

From discovery to ModelOps, we turn AI pilots into reliable, governed systems that move business KPIs.

From hype to engineered value

Organizations today face a paradox: executive urgency to adopt AI is at an all‑time high, while sustainable productivity gains require methodical investment in data quality, evaluation, and operational excellence. Our perspective is grounded in delivery: value emerges when AI is treated as a system, not a feature. That means defining decision boundaries, establishing human‑in‑the‑loop checkpoints, and baking evaluation into the lifecycle so that accuracy, robustness and safety trend up while unit cost trends down. We begin with strategic use case selection tied to measurable business KPIs, then design agent patterns that decompose tasks, reason over tools and knowledge, and interact safely with core systems. Each workflow is instrumented for latency and cost, and every change moves through guardrails so regressions are caught early. This is the engineering that turns pilots into platforms.

Agent architectures and tool orchestration

Most enterprise tasks are composite. Our agent designs separate planning from execution and use structured tools for retrieval, actions, and verification. We implement deterministic planners where possible and employ constrained generation with schemas when free text is risky. Retrieval pipelines blend hybrid search, recency signals and business rules to reduce hallucination and cost. Tooling includes evaluators, feature stores for prompts and contexts, and circuit‑breakers to protect downstream systems. The result is reliable behavior that can be measured, optimized and audited. We use offline and online evaluation to compare strategies and automatically promote better policies under cost and safety constraints.

Data readiness and governance

AI amplifies both the strengths and weaknesses of your data. We assess lineage, consent, access controls, PII handling and retention. Where gaps exist, we deploy pragmatic governance patterns: searchable data contracts, schema registries, and documentation that ties datasets to owners and SLAs. We instrument feature and embedding generation with versioning so experiments are reproducible. Confidential data is isolated with approved transformations, and sensitive outputs are filtered with policy enforcement. These practices reduce rework and speed compliance reviews, while improving the quality of inputs that drive agent accuracy and stability.

Evaluation, safety and cost control

We treat evaluation as a product: curated datasets, rubric design, golden answers, and automated scoring pipelines. We track accuracy, faithfulness, toxicity, bias, jailbreak resistance, latency and dollars per interaction. Business metrics—cycle time, conversion, recovery rate—are attached to the same events so trade‑offs are explicit. Budget guards prevent runaway costs and can dynamically switch models or strategies when thresholds are reached. These controls make AI predictable for finance and operations while giving teams the freedom to iterate quickly inside safe budgets.

ModelOps and delivery at scale

Our ModelOps foundation includes versioned artifacts, CI for prompts and retrieval configuration, progressive delivery with shadow and canary modes, and observability that traces every decision. We maintain runbooks for rollback and incident handling, with dashboards that show accuracy and safety alongside SLOs. Platform teams gain paved roads for new use cases, and security teams get the auditability they need. Over time, the platform becomes a flywheel: new capabilities ship faster because the underlying evaluation, governance and operations are already in place.

Operating model and talent

Great systems require great teams. We staff small principal‑led squads that own outcomes, pair with client engineers, and transfer practices through enablement. Chapters for Architecture, MLOps, SRE, Data Engineering and AppSec ensure consistency across engagements. Nearshore hubs enable same‑time‑zone iteration; remote hubs provide follow‑the‑sun operations. This blended model improves velocity, reduces total cost of change, and addresses persistent talent constraints in the market.

Why now

  • Demand signal: Analysts project multi-trillion tech spend growth through 2026 with two-thirds in software and services (Forrester). Enterprises fund AI programs while expanding managed services (ISG).
  • Adoption reality: Most firms expect AI to transform work by 2030 (WEF), yet productivity gains are uneven and require engineering discipline (OECD).
  • Maturity shift: Hype gives way to AI engineering — agents, evaluation, data quality, and ModelOps (Gartner coverage).

What we deliver

  • Use case discovery and prioritization tied to business KPIs
  • Agent patterns with tool use and orchestration
  • RAG systems with evaluation and observability
  • ModelOps: versioning, CI/CD, testing, rollout, cost control
  • Governance: policy, risk controls, human-in-the-loop, audit

How we measure value

  • Productivity: task cycle time, assisted vs. unassisted throughput, error rates
  • Quality: evaluation suites for accuracy, robustness, safety; human QA acceptance
  • Reliability: latency, tail p95, cost per 1k requests, regression guardrails
  • Business impact: conversion, CSAT, revenue lift, cost avoided

Proof and measures

We baseline and track cycle time, quality and unit cost. We report model/system metrics (latency, accuracy, robustness, safety) alongside business KPIs (conversion, throughput, savings) to show durable value.

References

Case example

AI agent for customer operations: Reduced handle time by 27% with assisted responses and automated after-call summaries. Evaluation suite tracked accuracy, safety and cost per interaction. Business KPIs improved NPS by 6 points and cut backlog by 18%.

KPIs: cycle time, first-contact resolution, model accuracy/safety, unit cost.