A forward deployed pod of 3–5 tkxel engineers joins your codebase to ship AI features, clear customer implementation backlogs and automate support, working in your repositories, sprints and SOC 2 controls, then hands the work over with docs and tests.
Book discovery callThe pod builds assistants, summarization, natural language search or agent workflows into your application, with tenant-scoped retrieval, per-tenant token budgets, evaluation suites in CI and feature flags, so product managers can release to design partners before general availability.
Agents and scripts map customer CSV exports and legacy schemas to your data model, flag mismatches for the implementation manager and generate configuration from scoping documents. Your onboarding backlog shrinks without hiring a services team you will later struggle to keep busy.
Agents classify tickets in Zendesk, Intercom or Salesforce Service Cloud, pull account context and logs, draft replies grounded in your docs and changelog, and escalate bugs to Jira with reproduction steps. Agents answer customers only on topics you have approved.
The pod clears the queue of customer-requested integrations with Salesforce, HubSpot, NetSuite or Snowflake, building connectors on your existing framework with contract tests, retry handling and the observability your platform team already uses.
Agents draft answers to customer security questionnaires from your policies and past responses, and collect control evidence from cloud, identity and ticketing systems for your SOC 2 auditor. Your security lead approves every answer before it goes to a prospect.
Agents join product telemetry, billing and CRM data in your warehouse to surface accounts with falling usage, stalled implementations or unresolved tickets, and write weekly account summaries for customer success managers, with every number linked to its query.
We’ll scope it, staff it and show a delivery plan in a 30-minute call.
Book a scoping callYour best people own the roadmap and the on-call rota, so AI work becomes a side project that never ships.
A prototype that works on one account fails on messy customer data, unusual configurations and production scale.
Prospects want to know where prompts go, how tenants are isolated and whether the feature sits inside your SOC 2 scope.
Without per-tenant limits and caching, usage-based model costs can erase the margin on a plan tier.
We pick the feature or backlog, set acceptance criteria with product and engineering, and onboard to your repositories, CI and environments under your access policies. Your security team checks our engineers against your SOC 2 vendor and personnel controls.
Deliverables: scope document, acceptance criteria, access checklist, architecture notes
The pod works in your sprints, follows your code review rules and ships behind feature flags. Every AI feature gets an evaluation suite, per-tenant cost tracking and logging that fits your existing observability.
Deliverables: merged pull requests, evaluation suite, cost dashboard, feature flags
We roll out to design partners first, measure quality and cost against the targets set in week one, and fix what real usage exposes. Your security team updates the system description and data flows for the next SOC 2 period.
Deliverables: release notes, quality report, updated data flow diagrams, customer documentation
Your engineers take ownership through paired work, runbooks and on-call shadowing. We leave no proprietary framework behind, only code and tests written in your style.
Deliverables: runbooks, architecture decision records, on-call guide, knowledge transfer sessions
We build in your repositories and environments from day one, so there is no separate rebuild between proof of concept and release.
Each pod is led by an engineer who has shipped AI systems before, drawn from tkxel’s team of around 150 AI professionals. The same people stay through delivery.
Autonomy matrices, audit trails, and human review are designed with your risk and security teams before release, backed by ISO 27001 controls.
Every engagement starts with agreed business metrics and evaluation sets. We report against them throughout, and the pod exits when your team can run the system.
Start with one feature tied to a workflow customers already do in your product, such as summarizing records, answering questions over their data or automating a multi-step task. Build it with tenant-scoped retrieval, an evaluation suite that runs in CI, per-tenant cost limits and a feature flag, then release to a few design partners. The hard parts are usually data quality across tenants and keeping quality stable as models change, not calling the model.
Keep ownership in-house, but you do not have to staff the first build entirely yourself. A forward deployed pod works in your repositories and sprints, so the result looks like your code and your team can maintain it. Buying a black-box platform is faster to start but harder to differentiate and to explain to enterprise buyers. One honest caveat: if no engineer on your side will own the feature after handover, wait until one can.
Scope every retrieval, memory and tool call to the tenant ID at the data layer, not in the prompt, so the model cannot be talked into reading another customer’s records. We add automated tests that attempt cross-tenant access, partition embeddings and caches by tenant, and log each request with tenant and user identifiers. Where customers require it, model calls can use regional endpoints or the zero-retention settings your model provider offers.
Yes. If they process customer data, they fall inside the system your auditor examines. Expect to update your system description, data flow diagrams, vendor list including the model provider, change management evidence and logging controls. We build with those controls in place, including pull request reviews, documented releases, access logging and subprocessor records, so the next audit period has evidence rather than gaps. Your auditor decides scope; we supply what they ask for.
They can shorten it, mainly by doing the mapping, data cleanup and configuration drafting that take up most of an implementation manager’s week. Agents read scoping documents and customer exports, propose a configuration and flag what needs a human decision. They do not replace conversations with the customer’s team about how they want to work, so expect faster projects rather than projects without people.
For a well-scoped feature, a pod typically has a version with design partners in 6–10 weeks and hands over within 10–14 weeks. Timelines stretch when the feature depends on data your platform does not yet capture, when enterprise customers require a security review before beta, or when quality targets are undefined. We set measurable quality and cost targets in week one so the release decision is not a matter of opinion.
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