Question 1

What does ReinforcedX actually do?

Accepted Answer

We design, train, and deploy production AI systems — agents, evaluation pipelines, and RL post-training — as a platform plus a hands-on implementation team. You get working software in production, not a slide deck.

Question 2

Which industries do you work with?

Accepted Answer

Financial services, healthcare, e-commerce, and technology are our deepest suites, with compliance-first architectures for regulated industries. The platform itself is industry-agnostic — suites package the domain expertise.

Question 3

Do we need an in-house ML team to work with you?

Accepted Answer

No. Most of our customers start with strong software engineers and no ML specialists. The four-week engagement is designed to leave your existing team owning the system — we train them as we build.

Question 4

How are you different from a traditional consultancy?

Accepted Answer

Consultancies bill for the roadmap and leave you the build. We ship the build. Every engagement ends with running software, an eval suite, and a runbook your team owns — and we are measured on whether it stays in production, not on hours logged.

Question 5

What is the smallest sensible place to start?

Accepted Answer

One high-volume, well-bounded workflow with a clear success metric — invoice matching, tier-one support triage, contract review. One agent live and measured beats five half-built ones, and it gives you the internal proof to fund the next five.

Question 6

How long does it take to get to production?

Accepted Answer

Four weeks is our standard implementation: discovery in week one, setup in week two, a shadow-mode pilot in week three, and handoff in week four. 98% of customers have their first agent live by week four.

Question 7

Do you work with our existing models and stack?

Accepted Answer

Yes. Reinforce is model-agnostic — Anthropic, OpenAI, Google, Mistral, and your own fine-tunes run through one interface, and we integrate with your existing cloud, data warehouse, and observability stack rather than replacing them.

Question 8

Can agents integrate with our internal tools?

Accepted Answer

Yes — that's usually the point. CRM, helpdesk, data warehouse, Slack, email, custom internal APIs: agents read from and act on your systems through scoped, audited tool connections.

Question 9

What do you need from our team during the four weeks?

Accepted Answer

One process owner who knows the workflow end to end, one engineer with access to the systems being integrated, and a weekly 45-minute review. That is genuinely it — we do not need a standing project committee.

Question 10

What happens after handoff?

Accepted Answer

We stay on-call for 30 days after handoff at no extra cost, then move to an optional support retainer. Most teams also keep a quarterly eval-review cadence with us to catch drift early.

Question 11

How do you measure whether the AI is actually good?

Accepted Answer

Every system ships with an evaluation suite: golden datasets built from your real cases, rubric-driven automated judges, and regression gates in CI. Quality becomes a number you track, not an opinion you debate.

Question 12

What is RL post-training, and do we need it?

Accepted Answer

Reinforcement learning post-training sculpts a pre-trained model toward your task using reward signals instead of labeled examples. You need it when prompting and fine-tuning plateau — typically for multi-step agent behaviors that must be reliable.

Question 13

What happens when the agent gets something wrong?

Accepted Answer

Low-confidence and high-stakes cases route to a human queue by design, and every failure is captured with full trace context. Those traces become new eval cases, so the same mistake gets caught automatically the next time.

Question 14

How do you stop the model from hallucinating?

Accepted Answer

Retrieval grounding with citation checks, structured outputs validated against a schema, and a claim-verification pass on anything customer-facing. Ungrounded answers are blocked before they ship, not apologised for afterwards.

Question 15

How do you handle model drift over time?

Accepted Answer

The eval suite runs continuously against live traffic samples, and regressions page us the same way a failing test would. When a provider silently updates a model, you find out from a dashboard — not from a customer.

Question 16

Who owns the models, data, and code?

Accepted Answer

You do. Everything we build inside your engagement — fine-tuned weights, datasets, eval suites, runbooks — is yours. We hand over the keys at the end of the engagement, and your team runs it.

Question 17

How do you handle data security and compliance?

Accepted Answer

SOC 2-aligned controls, deployment inside your cloud perimeter where required, per-user permission enforcement at retrieval time, immutable audit logs, and model-risk documentation for regulated deployments.

Question 18

Is our data ever used to train shared models?

Accepted Answer

Never. Your data trains your models only. We run with zero-retention provider settings by default, and anything fine-tuned on your data stays inside your tenancy.

Question 19

Can we run everything inside our own cloud?

Accepted Answer

Yes — VPC, on-prem, or your existing Kubernetes cluster. For regulated deployments this is the default: nothing leaves your perimeter, and we work within the network and identity controls you already run.

Question 20

Can you show an auditor how a decision was made?

Accepted Answer

Every agent run is logged with its inputs, retrieved context, tool calls, model version, and final output. Auditors get a reconstructable trail per decision, plus model-risk documentation mapped to your governance framework.

Question 21

How is pricing structured?

Accepted Answer

A platform subscription plus a fixed-scope implementation fee. Implementation is priced by engagement, not by the hour — you know the total cost before we start. Book a demo for a quote scoped to your use case.

Question 22

What does the total first year usually look like?

Accepted Answer

For a single-suite deployment, most teams land between a mid-five-figure implementation and an annual platform subscription sized to usage. We give you the full number in writing before week one — no change orders mid-build.

Question 23

Who pays for model inference costs?

Accepted Answer

You do, directly to your provider, at your own negotiated rates. We never resell tokens with a markup, and we actively tune routing and caching to bring that bill down — smaller models where they win, larger ones only where they earn it.

Question 24

What support do we get after the engagement ends?

Accepted Answer

30 days of on-call cover is included. After that, most teams take a retainer with a named engineer, quarterly eval reviews, and priority response — but it is optional, and plenty of teams run solo from day 31.

Environment	Obs	Act	PPO	SAC	TD3	TD7 ★
HalfCheetah-v4	17	6	1,744	11,060	9,850	17,433
Ant-v4	27	8	1,584	5,980	5,620	8,509
Hopper-v4	11	3	3,022	3,210	2,870	3,511
Walker2d-v4	17	6	2,008	4,110	3,765	4,838
Humanoid-v4	376	17	630	5,780	280	6,002

Environment	Obs	Act	PPO	SAC	TD3	TD7 ★
CartPole-v1	4	2	500	500	—	—
LunarLander-v2	8	4	254	262	—	—
BipedalWalker-v3	24	4	261	316	302	—
MountainCarContinuous-v0	2	1	91	94	90	—

Harness the power of artificial intelligence to drive innovation, boost efficiency, and unlock new possibilities for your organization.

Agent teams built for every department.

Financial Services AI Suite

Everything between a reward function and a deployable policy.

Distributed simulation

Algorithms, first-class

Reproducible runs

Adversarial evaluation

Observability built-in

Deploy anywhere

Teams shipping real outcomes with Reinforce.

How Kallix automated 30,000+ minutes of sales & support calls with AI voice agents

Bhartiya Didi — AI-guided triage connecting patients to the right specialist at ShardaCare

Scaling Batavia's digital store with AI-powered order and customer automation

Scaling Mandala's e-commerce operations with intelligent process automation

Scaling Pandawa's SaaS operations — faster onboarding, less overhead

Six channels, one loop.

Every run, span by span.

Quality as a number.

Right model, right step.

Claims that cite.

Shadow, then some, then all.

Cost per resolved task.

The Delivery Playbook

From signed to shipped in four weeks.

What teams actually measure in production.

Engineering notes from the blog.

Questions, answered.

Ready to refine
your workflow?

Enterprise AI That Ships.