GSAI · 2026 · 06 · 0025
AI Solution BriefTurn enterprise content from a manual workshop into a brand-consistent, multi-channel, reviewable production line
An end-to-end AI content pipeline for marketing, e-commerce, education, and media teams — from Brief → multimodal generation → brand-consistency check → collaborative review → multi-channel publishing. A team of 5 outputs what 50 used to, with zero brand drift.
Content production is moving from manual workshops to industrial pipelines
ChatGPT / Midjourney / Sora dropped the cost of single-point generation to zero. The real enterprise pain isn't 'can we generate' — it's 'is it usable, on-brand, channel-ready, and reviewable'. Stacking point tools is not a pipeline. What content teams need is to wrap AI generation inside a full industrial flow: brand-consistent, collaboratively reviewed, multi-channel published.
Four old problems point AI tools can't solve
Gluing ChatGPT + Midjourney + CapCut + analytics doesn't yield a pipeline. The three sticky middle steps — assembly, review, brand-checking — actually got worse with point tools.
One piece needs N variants — manual assembly explodes
A new SKU needs copy + hero image + detail long-image + TikTok script + RED post + Weibo card + IG variant. Each channel has its own size, length, and tone rules. Doing this combinatorially by hand: 3 people × 5 days per launch.
Generic LLMs don't know your brand voice
ChatGPT writes in 'generic AI voice'; Midjourney paints in 'generic AI aesthetic'. Conversion suffers. Your brand's product points, tone, visual specs, and best-performing assets simply can't be fed into a generic model.
Multi-version × multi-channel × multi-language drowns spreadsheets
One post in CN/EN/JA/KO × 6 channels × A/B = 48 variants. Version naming is manual, approval is via group chat, performance lives in 6 different consoles. Last quarter's best variant? Usually unfindable.
AI mistakes are slower to fix than to redo
AI gets the product name wrong, slips a typo into the image, mispronounces the brand in voiceover. All caught by humans, line by line. Without a systematic review flow, every fix triggers a re-run — and the round-trip is slower than starting fresh.
One platform, five immediately landable content scenarios
We don't build 'another AI writing tool'. We absorb your existing content production loop. Five scenarios below share one foundation: brand DB + pipeline orchestration + review system + channel delivery.
E-commerce SKU → multi-channel assets
Each SKU launch auto-produces copy + hero + detail + TikTok / RED / IG variants
Short video script + storyboard + AI VO
Topic → script → storyboard → AI voice-over → rough cut, humans only polish
Private community / public-account content calendar
Weekly calendar → batch generation + brand lock + schedule + data return
Multi-language global marketing localization
Core piece → CN/EN/JA/KO + per-market localization (not just translation)
Course marketing + knowledge shorts
Curriculum → enrollment copy + intro long-image + 30s short + matching posters
Three core capabilities, broken down with real UI
We zoom in on the three hardest-to-land parts of AI content (brand consistency, multimodal chaining, collaborative review). Each has a real UI, verifiable outputs, and a traceable accountability chain.
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Brand Lock Engine
Make AI sound like you, not like ChatGPT
Inject your Style Guide (visual specs + tone of voice + banned words + must-use phrases) + product DB (SKU / claims / pricing / use cases) + best historical assets as constraints into every generation. Output is on-brand by default, not by edit.
- Style Guide injection (tone / palette / fonts / banned / must-use)
- Live product DB binding — verifies SKU / claims / price for factuality
- Best historical assets as few-shot examples
- Outputs include Brand Match Score + highlighted deviations
- Style Guide learns continuously — review edits flow back
Multi-modal Pipeline
Copy → poster → video → social variants, one connected flow
Not 4 disconnected tools — a single pipeline with shared context. The selling point picked by the copy node flows to the poster; the poster's visual style flows to the video; the video's keyframes spawn the social variants. Each node is independently editable, but global style stays in sync.
- 5-format chained generation (copy / hero / long-image / video / social)
- Upstream outputs become downstream context — style stays consistent
- Re-run any node — downstream auto-rebuilds
- 8+ channel adapters (size / length / tone / hashtag auto-adapt)
- Node-level control — override any node's prompt independently
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Collaborative Review & Version Control
Draft → annotate → approve → publish, fully traced
Replace 'spreadsheets + group chat' review with one system. Every version is numbered, every edit attributed, every approval logged, every publish destination tracked. Performance data flows back to the original piece and feeds Brand Lock — the model gets more on-brand over time.
- Auto version numbering + any-version diff
- Inline annotations + @ collaboration + role permissions
- Multi-stage approval flow (create → ops → legal → channel)
- One-click multi-channel publish — adapts to each platform's API
- Post-publish performance flows back to the source — feeds Brand Lock
An observable, intervenable AI content pipeline + multimodal architecture
We treat content production as a pipeline, not a black box. Each layer is independently swappable — models can shift from SaaS to self-hosted, channels can scale as needed.
5-stage pipeline
From Brief to multi-channel publish, every step is observable, intervenable, and replayable. Quality drops trace to a specific stage.
Structured intake of product, selling points, target channels, and style intent — landed via Brief DSL as a pipeline-consumable task spec.
Copy → hero → long-image → video → social, a multi-node DAG sharing context. Each node routes to the best model; context flows between nodes for style consistency.
Rules engine + LLM self-check + visual similarity check against Style Guide / product DB / banned words / visual specs — output is a Brand Match Score and a deviation report.
Auto version numbering + inline annotations + multi-stage approval DAG. Approval trails trace every edit, every approver, every model version.
One-click publish to 8+ channels, adapters auto-fit each API. Post-publish data (impressions / CTR / conversion / GMV) returns to the source and feeds Brand Lock.
5-layer architecture
五层各司其职、可独立替换演进。任何一层都可以从 SaaS 切到自部署,从 GPT 切到 Claude / DeepSeek,从微信切到 X 等任意渠道。
Three representative scenarios, anonymized
Three abstracted, anonymized scenarios to help you judge what the platform can land in your organization.
Large FMCG brand · 200+ marketing assets / month
Onboarded 230 SKUs + Style Guide. A 5-person content team's output grew from 60 to 240 pieces per month; brand consistency from 70% to 96%.
E-commerce SaaS · multi-channel SKU automation
100+ merchant tenants — every new SKU auto-generates copy + hero + detail + TikTok / RED / IG variants.
EdTech platform · course marketing + knowledge shorts
Courses DB + instructor voice onboarded. Each new course auto-spawns enrollment poster + intro long-image + 30s short + 15s vertical with AI voice-over.
Representative anonymized scenarios; actual project data is delivered separately under partner NDA.
Brand assets stay in; generated content is compliant and traceable
Content security spans brand assets, user data, and generation compliance. All three are first-class.
On-premise deployment
Full on-prem / hybrid / domestic stack supported. Style Guides, product DBs, generation history all stay inside — never flow to third-party training.
Brand asset isolation
Each tenant's Style Guide / product DB / asset library is independently encrypted; never enters cross-tenant training or shared pools.
AIGC compliance markers
Generated content automatically carries AIGC labels (per PRC AIGC regulations) — supports watermarking and metadata embedding.
Sensitive-word & compliance check
Industry sensitive lexicons + policy rules + sensitive-topic auto-block. Every piece must pass before publish.
Copyright compliance
Training data provenance traceable; image / video can use self-hosted models to avoid commercial copyright risk; reference assets auto-checked against rights libraries.
Operation audit
Who generated what, who approved what, where it published, what it earned — fully logged and exportable for compliance.
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If you have a concrete workflow AI hasn't solved yet, let's figure out the right approach together.
We unpack the workflow with you, judge whether AI is worth using and which approach makes the most sense, then come back within 5 business days with a practical initial plan and estimate.