{"title":"Artificial Intelligence AI","description":"\u003ch1 class=\"cons-title\"\u003eAI application development\u003c\/h1\u003e\u003cp\u003eWe build AI features and integrations for business software: LLM chat, agents, RAG, and workflow automation on stacks you already run. Hong Kong-led delivery with clear scope and engineering owners.\u003c\/p\u003e\u003cp\u003eWe use commercial and open models plus orchestration tools where they fit your security and deployment model. Products below cover agents, generative AI, enterprise integration, and related services.\u003c\/p\u003e\u003cp\u003e\u003ca href=\"\/pages\/contact\"\u003eContact us\u003c\/a\u003e with your use case and data constraints. For hardware-backed AI, see \u003ca href=\"\/collections\/ai-engineering\"\u003eAI Engineering\u003c\/a\u003e and \u003ca href=\"\/products\/physical-ai-engineering\"\u003ePhysical AI\u003c\/a\u003e.\u003c\/p\u003e","products":[{"product_id":"generative-ai-development","title":"Generative AI Development","description":"\u003cp\u003eSoftware development utilizing the latest AI artificial intelligence technologies, including OpenAI ChatGPT, DeepSeek, Anthropic Claude, Meta Llama, Baidu AI, Alibaba Qwen. 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By combining edge computing, predictive analytics, and smart sensors, organizations achieve significant growth, competitive edge, and reshaping of industrial efficiency.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"How does Techwall ensure quality control in AIoT manufacturing?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Techwall integrates quality control at every stage of AIoT manufacturing, from PCB design to production lines. Smart sensors and digital twins allow real-time monitoring, anomaly detection, and predictive analytics to prevent defects. 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Few can ship one that survives production traffic, changing APIs, and real user behavior. Techwall's \u003cstrong\u003eAI Agent Design\u003c\/strong\u003e service closes that gap: we define the architecture, tool boundaries, and operational model before a single line of integration code is written.\u003c\/p\u003e\u003cp\u003eWe work with product leaders, CTOs, and innovation teams who need agents that connect to real systems — CRMs, ERPs, factory MES, device fleets, and custom APIs — not chatbots that stop at the browser.\u003c\/p\u003e\u003ch3\u003eWhat We Design\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eSingle-agent workflows\u003c\/strong\u003e — focused assistants with clear scope, tool access, and escalation paths.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMulti-agent systems\u003c\/strong\u003e — planner\/executor patterns, specialist sub-agents, and supervised handoffs.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTool \u0026amp; MCP layers\u003c\/strong\u003e — structured connectors so models invoke APIs safely and predictably.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGuardrails\u003c\/strong\u003e — input\/output filters, policy checks, approval gates, and audit trails.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePhysical AI agents\u003c\/strong\u003e — agents that bridge cloud reasoning with edge devices and manufacturing workflows (\u003ca href=\"\/pages\/physical-ai\"\u003ePhysical AI hub\u003c\/a\u003e).\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eOur Design Process\u003c\/h3\u003e\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eDiscovery\u003c\/strong\u003e — map stakeholders, data sources, latency requirements, and compliance constraints.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eUse-case framing\u003c\/strong\u003e — define agent roles, success metrics, and what stays human-owned.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eArchitecture draft\u003c\/strong\u003e — diagrams for agent graph, tool registry, auth model, and deployment topology.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePrototype scope\u003c\/strong\u003e — thin vertical slice to validate tool calls and failure modes.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eProduction roadmap\u003c\/strong\u003e — phased rollout tied to \u003ca href=\"\/products\/ai-agent-harness-design\"\u003eharness \u0026amp; eval design\u003c\/a\u003e and optional \u003ca href=\"\/products\/forward-deployed-engineering\"\u003eforward deployed engineering\u003c\/a\u003e.\u003c\/li\u003e\n\u003c\/ol\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eArchitecture decision record (ADR) and system diagrams\u003c\/li\u003e\n\u003cli\u003eTool\/MCP specification with schemas and error contracts\u003c\/li\u003e\n\u003cli\u003eGuardrail \u0026amp; security checklist\u003c\/li\u003e\n\u003cli\u003ePilot backlog with acceptance criteria\u003c\/li\u003e\n\u003cli\u003eTechnology recommendations (models, frameworks, observability) — pragmatic, not a laundry list\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eHow This Differs From Integration\u003c\/h3\u003e\u003cp\u003eOur \u003ca href=\"\/products\/ai-agent-enterprise-integration\"\u003eAI Agent Enterprise Integration\u003c\/a\u003e service focuses on wiring agents into N8N, Make, Zapier, and existing automation stacks. \u003cstrong\u003eAgent Design\u003c\/strong\u003e is upstream: the blueprint that makes integration durable. Many clients engage Design first, then Integration or FDE for rollout.\u003c\/p\u003e\u003ch3\u003eRelated Services\u003c\/h3\u003e\u003cp\u003e\u003ca href=\"\/collections\/ai-engineering-service\"\u003eAI Engineering\u003c\/a\u003e · \u003ca href=\"\/products\/ai-agent-harness-design\"\u003eAgent Harness \u0026amp; Eval\u003c\/a\u003e · \u003ca href=\"\/products\/forward-deployed-engineering\"\u003eForward Deployed Engineering\u003c\/a\u003e · \u003ca href=\"\/pages\/physical-ai\"\u003ePhysical AI\u003c\/a\u003e\u003c\/p\u003e\u003ch3\u003eFrequently Asked Questions\u003c\/h3\u003e\u003cp\u003e\u003cstrong\u003eDo you build the agent or only design it?\u003c\/strong\u003e\u003cbr\u003eBoth. Design can be a standalone engagement; we often continue into build, harness design, and deployment.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhich models and frameworks do you support?\u003c\/strong\u003e\u003cbr\u003eWe are model-agnostic — OpenAI, Anthropic, open-weight models, and edge deployments. Framework choices depend on your stack; we document trade-offs in the architecture phase.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eCan you design agents for regulated industries?\u003c\/strong\u003e\u003cbr\u003eYes. We incorporate audit logging, data residency, and human approval steps into the design from day one.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eHow long does agent design take?\u003c\/strong\u003e\u003cbr\u003eTypical discovery + architecture: 2–4 weeks for a focused use case; larger multi-agent programs are phased.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWe already have an internal team — can you augment them?\u003c\/strong\u003e\u003cbr\u003eYes. We deliver architecture packages your team can implement, or pair via forward deployed engineering.\u003c\/p\u003e\u003cscript type=\"application\/ld+json\"\u003e{\"@context\":\"https:\/\/schema.org\",\"@type\":\"FAQPage\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"Do you build the agent or only design it?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Both. Design can be a standalone engagement; we often continue into build, harness design, and deployment.\"}},{\"@type\":\"Question\",\"name\":\"Which models and frameworks do you support?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"We are model-agnostic — OpenAI, Anthropic, open-weight models, and edge deployments. Framework choices depend on your stack.\"}},{\"@type\":\"Question\",\"name\":\"Can you design agents for regulated industries?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Yes. 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Techwall's \u003cstrong\u003eAgent Harness \u0026amp; Eval Design\u003c\/strong\u003e service gives you a repeatable way to measure agent quality before release and after every change.\u003c\/p\u003e\u003cp\u003eWe design harnesses that fit your release process — whether you ship weekly prompts, monthly model upgrades, or continuous deployments to thousands of users.\u003c\/p\u003e\u003ch3\u003eWhy Eval Harnesses Matter\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eRegression safety\u003c\/strong\u003e — catch broken tool schemas and policy violations before users do.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eModel migration\u003c\/strong\u003e — compare GPT, Claude, or open-weight models on the same scenarios.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCost control\u003c\/strong\u003e — benchmark token use and latency per workflow.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCompliance evidence\u003c\/strong\u003e — documented test results for internal audit and customer trust.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eWhat We Build (Design + Implementation Guidance)\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eScenario libraries covering happy paths, edge cases, and known failure modes\u003c\/li\u003e\n\u003cli\u003eAutomated runners integrated with GitHub Actions, GitLab CI, or your pipeline\u003c\/li\u003e\n\u003cli\u003eTracing hooks (OpenTelemetry-compatible patterns) for production vs eval parity\u003c\/li\u003e\n\u003cli\u003eHuman review queues for subjective or high-risk outputs\u003c\/li\u003e\n\u003cli\u003eDashboards: pass rate, cost per eval run, drift over time\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eOur Process\u003c\/h3\u003e\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eInventory\u003c\/strong\u003e — catalog agent capabilities, tools, and business-critical outcomes.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMetric design\u003c\/strong\u003e — define what \"good\" means (accuracy, safety, completion rate, user satisfaction proxy).\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eHarness architecture\u003c\/strong\u003e — choose assertion types, dataset format, and environment isolation.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003ePilot suite\u003c\/strong\u003e — 20–50 scenarios that block bad releases.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eScale-up\u003c\/strong\u003e — expand coverage, schedule runs, tie to \u003ca href=\"\/products\/forward-deployed-engineering\"\u003eFDE\u003c\/a\u003e rollout checkpoints.\u003c\/li\u003e\n\u003c\/ol\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eEval harness specification and repository structure\u003c\/li\u003e\n\u003cli\u003eInitial scenario dataset (JSON\/YAML) with expected behaviors\u003c\/li\u003e\n\u003cli\u003eCI integration guide and quality gates\u003c\/li\u003e\n\u003cli\u003eObservability map: what to log in dev vs production\u003c\/li\u003e\n\u003cli\u003eModel upgrade playbook\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003ePairs With Agent Design\u003c\/h3\u003e\u003cp\u003eBest results come when \u003ca href=\"\/products\/ai-agent-design\"\u003eAI Agent Design\u003c\/a\u003e defines tool contracts and success metrics upfront — we embed eval hooks into the architecture. Already have a live agent? We reverse-engineer scenarios from logs and incident history.\u003c\/p\u003e\u003ch3\u003eRelated Services\u003c\/h3\u003e\u003cp\u003e\u003ca href=\"\/collections\/ai-engineering-service\"\u003eAI Engineering\u003c\/a\u003e · \u003ca href=\"\/products\/ai-agent-design\"\u003eAI Agent Design\u003c\/a\u003e · \u003ca href=\"\/products\/forward-deployed-engineering\"\u003eForward Deployed Engineering\u003c\/a\u003e · \u003ca href=\"\/pages\/physical-ai\"\u003ePhysical AI\u003c\/a\u003e\u003c\/p\u003e\u003ch3\u003eFrequently Asked Questions\u003c\/h3\u003e\u003cp\u003e\u003cstrong\u003eDo you use LLM-as-judge for evaluations?\u003c\/strong\u003e\u003cbr\u003eSometimes — we combine LLM judges with deterministic checks on tool arguments, JSON shape, and policy rules to reduce false positives.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eCan you eval agents that call live APIs?\u003c\/strong\u003e\u003cbr\u003eYes. We design sandbox mocks, recorded fixtures, and staged environments so evals stay fast and safe.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWe use LangSmith \/ Braintrust \/ custom tools — can you work with that?\u003c\/strong\u003e\u003cbr\u003eYes. The harness design is tool-agnostic; we document integration patterns for your stack.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eHow is this different from QA testing?\u003c\/strong\u003e\u003cbr\u003eTraditional QA assumes deterministic outputs. Agent evals measure distributions, tool correctness, and safety over many runs — closer to ML ops than manual test scripts.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat if our agent is embedded in hardware?\u003c\/strong\u003e\u003cbr\u003eWe design edge-aware evals including latency budgets and offline fallbacks — see \u003ca href=\"\/pages\/physical-ai\"\u003ePhysical AI\u003c\/a\u003e.\u003c\/p\u003e\u003cscript type=\"application\/ld+json\"\u003e{\"@context\":\"https:\/\/schema.org\",\"@type\":\"FAQPage\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"Do you use LLM-as-judge for evaluations?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"We combine LLM judges with deterministic checks on tool arguments, JSON shape, and policy rules to reduce false positives.\"}},{\"@type\":\"Question\",\"name\":\"Can you eval agents that call live APIs?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Yes. We design sandbox mocks, recorded fixtures, and staged environments so evals stay fast and safe.\"}},{\"@type\":\"Question\",\"name\":\"Can you work with LangSmith or Braintrust?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Yes. The harness design is tool-agnostic; we document integration patterns for your stack.\"}},{\"@type\":\"Question\",\"name\":\"How is agent eval different from QA testing?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Agent evals measure distributions, tool correctness, and safety over many runs — closer to ML ops than manual test scripts.\"}},{\"@type\":\"Question\",\"name\":\"Can you eval agents embedded in hardware?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Yes. We design edge-aware evals including latency budgets and offline fallbacks.\"}}]}\u003c\/script\u003e","brand":"Tech Wall Electronics","offers":[{"title":"Default Title","offer_id":46479071805576,"sku":null,"price":0.0,"currency_code":"HKD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0641\/6692\/0328\/files\/Artificial_Intelligence_AI_Development_Service_-_AI_Agents_a666078b-fde8-4412-9fb9-65d0d80b30c1.png?v=1788428295"},{"product_id":"forward-deployed-engineering","title":"Forward Deployed Engineering","description":"\u003ch2\u003eForward Deployed AI Engineering\u003c\/h2\u003e\u003cp\u003eThe hardest part of AI is not the model — it is adoption. \u003cstrong\u003eForward Deployed Engineering (FDE)\u003c\/strong\u003e puts Techwall engineers beside your team during the weeks that matter: pilot launch, factory integration, executive demos, and the first production incidents.\u003c\/p\u003e\u003cp\u003eWe combine software engineering, agent operations, and — when needed — hardware\/manufacturing context from our \u003ca href=\"\/pages\/physical-ai\"\u003ePhysical AI\u003c\/a\u003e practice so deployments work in the environment you actually operate in.\u003c\/p\u003e\u003ch3\u003eWhen FDE Is the Right Fit\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eYou have a designed agent (\u003ca href=\"\/products\/ai-agent-design\"\u003eAI Agent Design\u003c\/a\u003e) but lack bandwidth to integrate with legacy systems.\u003c\/li\u003e\n\u003cli\u003ePilot success requires presence on the factory floor, in clinics, or with field technicians.\u003c\/li\u003e\n\u003cli\u003eInternal teams are strong on product but new to agent observability and eval gates (\u003ca href=\"\/products\/ai-agent-harness-design\"\u003eHarness \u0026amp; Eval\u003c\/a\u003e).\u003c\/li\u003e\n\u003cli\u003eStakeholders need confidence before a multi-site or multi-region rollout.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eEngagement Models\u003c\/h3\u003e\u003ctable\u003e\n\u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eModel\u003c\/th\u003e\n\u003cth\u003eBest for\u003c\/th\u003e\n\u003cth\u003eTypical duration\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePilot embed\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eSingle site or department go-live\u003c\/td\u003e\n\u003ctd\u003e4–8 weeks\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eRegional pod\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eMulti-team rollout with shared playbook\u003c\/td\u003e\n\u003ctd\u003e3–6 months\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eOffice hours + sprints\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eMature internal team needing expert backup\u003c\/td\u003e\n\u003ctd\u003eOngoing retainer\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\u003ch3\u003eWhat FDE Engineers Do On the Ground\u003c\/h3\u003e\u003col\u003e\n\u003cli\u003eShadow workflows and validate agent assumptions with real users.\u003c\/li\u003e\n\u003cli\u003eWire integrations, fix tool failures, tune prompts with live feedback.\u003c\/li\u003e\n\u003cli\u003eRun eval suites before each promotion to production.\u003c\/li\u003e\n\u003cli\u003eTrain champions who own the system after we step back.\u003c\/li\u003e\n\u003cli\u003eDocument edge cases discovered only in the field.\u003c\/li\u003e\n\u003c\/ol\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eRollout plan with milestones and rollback procedures\u003c\/li\u003e\n\u003cli\u003eUpdated integration code and configuration in your repos (IP stays yours)\u003c\/li\u003e\n\u003cli\u003eTraining sessions and recorded walkthroughs\u003c\/li\u003e\n\u003cli\u003eHypercare report: incidents, fixes, recommended next phase\u003c\/li\u003e\n\u003cli\u003eHandoff checklist for internal ops or managed support\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eGlobal Coverage\u003c\/h3\u003e\u003cp\u003eHeadquartered in Hong Kong with engineering and manufacturing in southern China, Techwall schedules overlap with US, European, and Asia-Pacific teams. On-site visits are arranged for pilot kickoff, critical integrations, and executive reviews; day-to-day work is often remote pair programming with your engineers.\u003c\/p\u003e\u003ch3\u003eRelated Services\u003c\/h3\u003e\u003cp\u003e\u003ca href=\"\/collections\/ai-engineering-service\"\u003eAI Engineering\u003c\/a\u003e · \u003ca href=\"\/products\/ai-agent-design\"\u003eAI Agent Design\u003c\/a\u003e · \u003ca href=\"\/products\/ai-agent-harness-design\"\u003eAgent Harness \u0026amp; Eval\u003c\/a\u003e · \u003ca href=\"\/collections\/manufacturing-services\"\u003eManufacturing\u003c\/a\u003e\u003c\/p\u003e\u003ch3\u003eFrequently Asked Questions\u003c\/h3\u003e\u003cp\u003e\u003cstrong\u003eIs FDE the same as staff augmentation?\u003c\/strong\u003e\u003cbr\u003eSimilar delivery model, different outcome. FDE is outcome-focused on agent rollout and handoff — not open-ended body-shop staffing.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eCan FDE engineers work on our VPN and repos?\u003c\/strong\u003e\u003cbr\u003eYes, under your security policies. We follow least-privilege access and document everything we touch.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eDo you offer FDE without prior design work?\u003c\/strong\u003e\u003cbr\u003eYes, but we recommend at least a short discovery so scope and success metrics are clear.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eCan FDE support hardware + software together?\u003c\/strong\u003e\u003cbr\u003eYes — a core Techwall differentiator. We deploy agents that interact with devices, edge gateways, and MES systems.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat happens after the embed ends?\u003c\/strong\u003e\u003cbr\u003eWe transition to your team with runbooks, optional retainer for model upgrades, or ongoing \u003ca href=\"\/collections\/ai-engineering-service\"\u003eAI Engineering\u003c\/a\u003e support.\u003c\/p\u003e\u003cscript type=\"application\/ld+json\"\u003e{\"@context\":\"https:\/\/schema.org\",\"@type\":\"FAQPage\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"Is forward deployed engineering the same as staff augmentation?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Similar delivery model, different outcome. FDE is outcome-focused on agent rollout and handoff — not open-ended staffing.\"}},{\"@type\":\"Question\",\"name\":\"Can FDE engineers work on our VPN and repos?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Yes, under your security policies. 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