Service · ai integration

Production-grade AI agents, not prototypes.

Most AI features break the first time a real user touches them. We build the ones that don't.

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What's included

  • Use-case discovery and an eval bench in week one
  • Agent orchestration with planning, tool use, memory, and retries
  • RAG over proprietary data, encrypted and permission-aware
  • Guardrails, audit logging, red-team testing
  • Cost and latency budgets enforced per agent run
  • Observability dashboard for runs, tool calls, and traces
  • A model gateway your team can swap providers behind

Ideal for

  • Compliance copilots and research agents (FinTech)
  • In-product workflow agents and assistants (SaaS)
  • Internal knowledge agents and ops automation (any)

Outcomes

  • An eval suite that tells you when the model regresses
  • Guardrails and audit logs that survive a security review
  • Cost and latency instrumentation from day one

What we cover

Three lanes, one engagement. Most teams need all three at different points in the agent lifecycle; some come in for one and grow into the rest. Every lane is senior-led and tied to a written outcome before week one.

01. AI transformation

The work that happens before the first prompt is written. We audit your existing tooling, surface the use cases that actually justify an agent, and quietly retire the ones that won’t survive contact with production. Output is a phased roadmap, a written eval target per milestone, and a build-vs-buy recommendation per use case. The deliverable is a plan your team can execute, not a deck.

02. Agent design & deployment

The build. Agent orchestration with planning, tool use, memory, and retries. RAG over proprietary data, encrypted and permission-aware. Guardrails, audit logging, and red-team testing wired in before the first user touches it. Every prompt is versioned in git. Every tool call is typed. Every release goes out behind a flag, with an eval gate that fails the PR on regression.

03. Agent observability & operations

What keeps it running after launch. Traces of every agent run, monitored against your golden set and your live cost budget. Dashboards your on-call engineer needs at 3am, alerts that fire before the customer notices, and a feedback loop that promotes real user runs into the eval bench so the agent gets stronger every week. We hand this over, or we keep running it for you.

The work, at a glance

SYS-02 · AGENT STACK EVAL-DRIVEN

  1. Surface

    Agent UIs · Copilots · Streaming responses · Citations

  2. Orchestration

    Planning · Tool calls · Memory · RAG retrieval

  3. Models

    Gateway · Cost + latency telemetry · Provider swap

  4. Trust

    Evals · Guardrails · Audit logs · Red-team replay

A prototype answers questions. A production agent shows its work, fails safely, and tells you when it regresses.

The difference between an agent demo and an agent people rely on is the bottom two layers. We build the eval bench before the prompt, and the audit log before the first tool call. The fun part of the stack is supposed to be at the top.

How we build for production

The same patterns show up in every agent engagement we ship:

  • Golden set — a hand-curated suite of 100–500 runs that defines “good enough” before the agent touches production. Updated weekly as the surface area grows.
  • Eval harness — runs on every PR and fails the build on regression. Cost, latency, and quality scored together, not separately.
  • LLM gateway — single chokepoint for provider routing, key rotation, rate limits, and cost telemetry. Swap GPT for Claude on a Tuesday.
  • Prompt and tool versioning — prompts and tool schemas live in git, not in a notebook. Every change ships with its eval delta attached.
  • Run traces — every agent run is stored with its planning steps, tool calls, and final response. Replayable, debuggable, and fed back into the eval bench when a customer flags something off.
  • Shadow traffic — new prompts run silently against real requests for a week before they replace the live path. Zero customer impact.

We build this scaffolding before the first prompt is tuned. The fun part is supposed to be at the top of the stack, not in the rollout meeting.

What changes, in numbers

1,247
Eval cases shipped
98.4%
Red-team pass rate
$0.0083
Average cost per request
p95 1.4s
End-to-end latency

The 60-day arc

FIG-03 · 60-DAY PILOT FIXED SCOPE
DAY 0 DAY 60 01 · WEEK 1 02 · WEEKS 2–7 03 · WEEKS 8–9

  1. 01

    Use case & eval bench

    Week 1

  2. 02

    Build, ship behind flag, iterate

    Weeks 2–7

  3. 03

    Hardening, dashboards, handoff

    Weeks 8–9

Week 1 ends with an eval bench and a written threat model. Week 7 ends with a flagged rollout. Week 9 ends with your team owning the gateway and the dashboards.

What you walk away with

Agents in production behind a flag, an eval suite that runs on every PR, audit logs your security team can read, run traces your engineers can replay, and a model gateway you can swap providers behind on a Tuesday. No prompt-spaghetti. No vendor lock-in. No “AI POC” that lives in a notebook.

FAQFrequently asked

What teams ask before they engage.

What is the difference between an AI feature and an AI agent?
An AI feature is a single-call LLM surface: a chat box, a summary, a generate button. An AI agent plans, calls tools, retries, and remembers across steps to accomplish a multi-step task. Most production AI work in 2026 is agent-shaped, even when the UI looks like a single text box.
How does Hotreloads handle hallucinations and confident-but-wrong answers?
Three layers. A golden set of 100-500 hand-curated runs the model must pass before each release. A guardrail layer that returns "I don't know" under load instead of fabricating. An audit log per run so when something does slip through, the team can replay it and add it to the eval bench. Hallucinations are not eliminated; they are observable, contained, and improving every week.
Which model providers does Hotreloads support?
Anthropic Claude, OpenAI GPT, Google Gemini, and open-weights (Llama, Mistral, Qwen) via vLLM or Together. The LLM gateway pattern means provider routing is a config change, not a code change. We optimise for cost and latency per use case, not for vendor allegiance.
What does an agent engagement cost compared to a simple LLM feature?
A scoped agent engagement runs longer than a feature build because the eval bench, guardrails, gateway, and run traces are part of the production cost, not the prototype cost. The trade-off: an evaluated agent that survives real users from week one, versus a demo that breaks when a customer types something weird.
How does Hotreloads handle data privacy for regulated industries?
Permission-aware RAG (the retriever respects the caller's row-level permissions). Tenant-scoped vector indexes. No PII in prompts unless explicitly required and audit-logged. Provider-side data-retention disabled where the SLA allows. For FinTech engagements, the architecture is signed off by your compliance lead before week one.
What does the 60-day pilot engagement deliver by the end of week nine?
Week one ends with a written eval target and a threat model. Weeks two through seven ship the agent behind a feature flag, iterated against the golden set on every PR. Week nine closes with the agent in production, an LLM gateway your team controls, audit logs your security reviewer can read, and run traces your on-call engineer can replay. The eval harness runs on CI from day eight onward so regression surfaces before a customer does. Handoff includes written runbooks and a live observability dashboard scoped to your cost and latency budgets.
How does Hotreloads monitor eval scores, model drift, and refusal rates after launch?
Every agent run is stored as a trace and scored nightly against the golden set. Drift is measured as the gap between the current pass rate and the baseline set at the last release. Refusal rate is tracked as a separate metric in Grafana, because a guardrail triggering too often is a signal the prompt or the retrieval pipeline has degraded, not a sign the model is being cautious. When either metric crosses its threshold, an alert fires and a sample of failing runs is queued for manual review and added to the eval bench within 48 hours.
How is AI integration priced differently from standard feature engineering?
Feature engineering is typically time-and-materials: scope shifts, the invoice follows. AI integration engagements run fixed-scope with a written eval target per milestone. The fixed price covers the eval bench, LLM gateway, guardrails, and run traces, not just the prompt or the UI. A separate retainer covers post-launch monitoring and golden-set updates, priced per 50 eval cases maintained per month. This structure means the cost of making the agent reliable is visible before the statement of work is signed, not discovered during handoff.