Pigeon CloudAI ENGINEERING
EN
Talk to us
Main navigation

Pigeon Cloud · Enterprise AI engineering

From business case to production

AI that works.Beyond the demo.

We design and deliver governed agent systems for business-critical workflows. From orchestration and memory to evaluation and private deployment: a senior engineering partner, accountable to measurable outcomes.

Discuss your AI roadmap Explore our expertise

Enterprise software since 2015

agent.runtime

SYSTEM / REFERENCE

A production control plane

01

Task execution

Checkpoints · bounded retries
Harness
02

Context & memory

Session state · governed recall
Scoped
03

Release gate

Regression tests · human approval
Evals

Reference design · not live telemetry

Model-agnostic. Built around your constraints.

OpenAIClaudeGeminiDeepSeekQwen

2015

Founded in Suzhou

Enterprise software roots

Evals

Evidence-led delivery

Acceptance criteria before code

Human

Oversight by design

Approval for consequential actions

Private

Deployment options

On-premises, VPC or hybrid

01Expertise

A systems partner, not a prompt vendor.

We connect AI strategy, software engineering and operations. Every investment starts with a workflow worth improving and a clear definition of success.

Strategy

Find the highest-value workflow

Assess process volume, data readiness, exception handling and integration costs before committing to a build.

Business case · baseline · acceptance criteria
Engineering

Own the production boundary

Integrate with your systems, apply least-privilege access, and make failures observable and recoverable.

System integration · security · evaluation
Operations

Improve the system over time

Track quality, unit economics and model changes. Deliver maintainable software, not a dependency on a single model.

Cost per task · quality · operational handover

02Agent infrastructure

The infrastructure behind reliable agents.

Use the simplest architecture that meets the objective. Add multi-agent coordination only when task separation produces a measurable gain.

01 / Orchestration

Agents & multi-agent systems

Clear roles. Controlled handoffs.

Task-specific agents with explicit tools and responsibilities. For complex workflows, a supervisor coordinates specialists while people retain control of consequential actions.

  • Typed inputs and outputs; bounded delegation
  • Parallel work only where tasks are independent
  • Approval gates for financial or external actions
Business objective

Shorter processing cycles, measured against a single-agent baseline.

02 / Runtime

Agent harness & evaluation

Autonomy needs an operating boundary.

A harness is the execution layer around a model: tools, state, permissions, budgets and recovery. We engineer the runtime before expanding autonomy.

  • Checkpointing, idempotency and bounded retries
  • Sandboxing, tool allowlists and injection defenses
  • Trace replay, regression suites and release gates
Business objective

Lower failure-handling costs and a defensible path to production.

03 / Context

Short- & long-term memory

Remember what matters. Forget by design.

Short-term memory holds task state and compressed session context. Long-term memory stores approved knowledge and preferences across sessions—not an unrestricted transcript archive.

  • Session state, checkpoints and context compaction
  • Tenant-scoped retrieval with provenance and freshness
  • Consent, retention, deletion and memory-write controls
Business objective

Less repeated work without sacrificing privacy or knowledge quality.

04 / Integration

Skills, MCP & enterprise tools

Turn expertise into reusable capability.

Versioned skills package procedures and resources; MCP connects agents to tools and data. Both are governed like software, with ownership, tests and access boundaries.

  • Versioned skill libraries with evaluation fixtures
  • MCP servers and authenticated API integrations
  • Least-privilege execution and auditable tool calls
Business objective

Reusable workflows across teams with less integration duplication.

03Business use cases

Start where the economics are clear.

Priority use cases with repeatable demand and measurable value. We validate the opportunity with your data; ROI is an acceptance target, not a promise.

Service operations

Knowledge & support agents

Evidence-backed answers and controlled actions across internal support and customer service.

  • Permission-aware RAG with citations and freshness
  • Ticket triage, suggested replies and escalation
  • Measure resolution rate, handling time and cost per ticket

Business operations

Document & process automation

Turn document-heavy back-office work into reviewed, traceable workflows.

  • Extract and validate invoices, contracts and reports
  • Integrate with ERP/CRM and human approval queues
  • Measure exception rate, cycle time and cost per document

Engineering teams

AI-enabled software delivery

Put coding agents to work inside the safeguards of your engineering organisation.

  • Repository skills, issue-to-PR and test generation
  • Sandboxed execution, CI checks and reviewer approval
  • Measure lead time, review effort and escaped defects

04Delivery method

Evidence first. Scale second.

Each phase has an explicit decision gate. We do not turn an inconclusive proof of concept into a production commitment.

  1. 01 / Assess

    Establish the business case

    Map the workflow, risk, data access and current cost. Agree the success metrics and stop conditions.

    DeliverableOpportunity assessment and acceptance plan

  2. 02 / Validate

    Test on representative tasks

    Build a bounded pilot, compare architectures and models, and examine failure cases—not just average scores.

    DeliverableEvaluation set, baseline and go/no-go recommendation

  3. 03 / Deliver

    Integrate and release safely

    Implement permissions, approvals, observability and rollback. Roll out to a limited audience before widening access.

    DeliverableProduction system, runbooks and team handover

  4. 04 / Operate

    Govern quality and cost

    Review task outcomes, incidents and unit costs. Re-evaluate model upgrades before release.

    DeliverableOperational reporting and improvement backlog

05Reference architecture

A control plane, not a chain of prompts.

The harness coordinates execution. Memory and tools have separate permissions; evaluation and audit span every layer.

Illustrative architecture · adapted per engagement

Private / hybridModel-agnosticHuman oversightTenant isolation
  1. Request
  2. Policy
  3. Harness
  4. Approved action

L01

Execution

Bounded autonomy

  • Agent / supervisor
  • Task graph & checkpoints
  • Budget & approval gates

L02

Context

Governed memory

  • Short-term task state
  • Long-term approved memory
  • RAG, citations & freshness

L03

Capabilities

Controlled access

  • Versioned skills
  • MCP / business APIs
  • Model gateway & sandbox

L04

Assurance

Across every layer

  • Offline evals & trace replay
  • Observability & cost tracking
  • Identity, audit & retention

06Engineering standards

Engineering principles we bring to the table.

Methods used in project design—not claims of published products, certifications or platform partnerships.

Quality

Evaluate the full task

  • Representative datasets and failure taxonomy
  • Tool correctness, not just fluent answers
  • Human review of high-impact outcomes

Security

Constrain the blast radius

  • Least privilege and tenant boundaries
  • Untrusted-content and prompt-injection tests
  • Scoped memory and auditable actions

Economics

Optimize cost per successful task

  • Model routing based on measured quality
  • Caching and context budget controls
  • Latency, retry and exception-cost tracking

Bring us a workflow, not a buzzword.

Together we can determine where agents create value, what should stay deterministic, and what it takes to operate safely.

Discuss your architecture

07Engagement models

A partnership for each stage.

Advisory, delivery and ongoing operations. Scope, fees, timelines and service levels are agreed after assessment.

08Questions, answered

Questions worth asking before you build.

Clear boundaries make better partnerships.

When is a multi-agent system worth building?

When a workflow benefits from distinct expertise, independent parallel tasks or separated permissions. We first compare a deterministic workflow and a single agent; more agents are justified only if evaluation shows a useful gain after coordination costs.

What is an agent harness?

The harness is the runtime around the model. It controls tools, task state, context, permissions, budgets, retries and recovery. It is what turns a model interaction into an observable and governable business process.

How do short-term and long-term memory differ?

Short-term memory retains state within a task or session. Long-term memory persists selected knowledge across sessions. We separate them, define provenance and retention, isolate tenants and support deletion; memory is not permission to store everything.

How do you choose models and frameworks?

We evaluate available models against task success, latency, total cost and deployment constraints. OpenAI, Claude, Gemini, DeepSeek, Qwen and other options can be considered where available. No model or framework is presented as universally best.

Can sensitive data stay in our environment?

We can scope on-premises, VPC or hybrid delivery. Model licensing, hardware, network isolation, tool access and data residency must be assessed. Security controls are agreed per project; we do not claim certification or automatic regulatory compliance.

What does a successful engagement look like?

Success is agreed before implementation: task completion, handling time, exception rates, quality and cost per successful task. We offer assessment, milestone delivery and ongoing support. Timelines, ownership, acceptance and any SLA are defined in the contract—not inferred from a marketing page.

Talk to us

Build the next layer of your business.

Share your workflow, existing systems, data constraints and target outcome. We will discuss the right scope and engagement—not prescribe a framework before understanding the problem.

Direct contact
sbin@un-bug.com
Based in
16#B, Creative Industrial Park, 328 Xinghu Street, Suzhou Industrial Park, China

Usually within 1–2 business days

Discuss your AI roadmap

30-minute introductory call · No obligation

  • Business objective and current bottlenecks
  • Data, deployment and security constraints
  • Next steps and an appropriate engagement scope
Book an introductory call