Utaalamu / Kwa karibu
Mawakala wa AI na automatisheni
AI na mawakala ya uzalishaji, kulingana na biashara inavyoendeshwa — na binadamu kila mzunguko muhimu.
Fursa
Fanya hatua inayofuata
hatua bora zaidi.
Teams lose hours moving information between tools, cleaning catalogues, assembling research and repeating checks. Most agent projects fail on scope, data and unclear approval — not on the model. We start from one real task, prototype on sample data and keep every consequential decision human.
We scope one task and its source of truth first, then define permissions, review points, evaluation checks and recovery states before any model is connected. Tool and API integrations (including MCP-style tool access) are layered in with least privilege, and output is tested against edge cases with guardrails and audit trails.
- Kufanya kazi pamoja
- Kiongozi wa uwasilishaji mwenye jina + timu
- Wakati
- Discovery + prototype in 2–4 weeks; rollout scoped after
Tunachowasilisha
Mkazo kwenye sahihi.
- Task, input and approval mapping
- Tool + API integrations with scoped, least-privilege permissions
- Review-first agent interfaces and audit trails
- Agent evaluation, guardrails and recovery states
Unachochukua
Kitu unachoweza kuendesha.
- Workflow map with scope, permissions and review points
- Working prototype on sample data
- Review, approval and audit interfaces
- Evaluation plan, integration, docs and support handover
Chunguza mwingiliano
Angalia mtiririko ukifunguka.
Maonyesho ya kiolesura kinachofanya kazi na data ya sampuli. Chagua kazi, fuatilia maendeleo na hakiki matokeo yaliyopendekezwa.
See the system
think in steps.
Choose a workflow, inspect the sample input and review the proposed output. You make the final call.
Find inconsistencies in a small sample product catalogue.
Product: Wireless headphones · Category: Audio · Compatibility: missing · Related products use the Headphones category.
- 01Inspect sample product fields
- 02Compare naming and categories
- 03Prepare suggested corrections
Your review workspace.
Run the example to reveal the proposed changes.
This browser simulation demonstrates the workflow and interface. Its sample responses are predefined; it does not call an AI model, access a store or publish changes.
Kabla hatujaanza
Uwazi kidogo
huenda mbali.
01Mnahitaji nini kutoka kwetu kuanza?
Maelezo mafupi ya tatizo, kiungo cha tovuti au chombo cha sasa ikiwa kipo, na matokeo muhimu yangekuwaje. Huhitaji vipimo vilivyokamilika. Ufikiaji, DPA/NDA na mahitaji ya maudhui tunakubaliana wigo ukiwa wazi.
02Mnaweza kufanya kazi na tulichonacho?
Ndiyo. Tunaanza na usanidi na mipaka yenu. Uboreshaji makini au muunganisho unaweza kushinda ujenzi upya — tutaeleza chaguzi na masharti, na gharama, kabla hamjaahidi.
03Gharama, wakati na SLA hukubaliwaje?
Baada ya ugunduzi, pendekezo lililo wazi: matokeo, hatua, ratiba, msaada na masharti ya SLA. Kuanza kwa sahihi; kila badiliko hufafanuliwa upya kwa maandishi kabla ya kazi ya ziada.
Utaalamu unaohusiana