Question 1

What is AI automation and how is it different from RPA?

Accepted Answer

RPA records keystrokes against fixed screens. AI automation reasons over unstructured data — documents, emails, conversations — and acts across APIs. We combine both: deterministic rules for the predictable path, LLMs for ambiguity, with humans in the loop where the cost of an error is high.

Question 2

Which LLMs do you build on?

Accepted Answer

OpenAI GPT-class models, Anthropic Claude, Google Gemini, Azure OpenAI, and open-weight models (Llama, Mistral, Qwen) self-hosted on your VPC. We pick per workload — quality, latency, cost, data residency.

Question 3

How do you keep our data private?

Accepted Answer

Private deployments inside your AWS / Azure / GCP account, no training on your data, contractual zero-retention with model providers, customer-managed encryption keys, VPC peering, and PII redaction before any prompt leaves your boundary.

Question 4

Do you support on-premise or air-gapped deployments?

Accepted Answer

Yes. We deploy quantized open-weight models on customer hardware, with vLLM / TGI inference servers, internal vector stores, and no outbound calls. Used by clients in healthcare, defence and regulated finance.

Question 5

How do you prevent hallucinations?

Accepted Answer

Retrieval-grounded answers with source citations, strict JSON schemas via function calling, evaluation suites that run on every deploy, confidence thresholds that route low-confidence cases to humans, and continuous red-teaming.

Question 6

What does an AI automation engagement cost?

Accepted Answer

Pilots start around $25k and ship in 4–6 weeks. Production rollouts run $80k–$400k depending on integrations and scale. We also offer a monthly retainer for ongoing prompt, evaluation and model upgrades.

Question 7

How long until we see ROI?

Accepted Answer

Pilots are designed to repay themselves inside the first quarter. Most production deployments hit payback in 3–6 months — measured on hours saved, deflection rate, error reduction or revenue uplift.

Question 8

Can you integrate with our existing CRM / ERP / helpdesk?

Accepted Answer

Yes. Salesforce, HubSpot, Dynamics, SAP, Oracle, Zendesk, Intercom, ServiceNow, Jira and 100+ more via native APIs, webhooks, or our integration layer.

Question 9

Do you build agents or just chatbots?

Accepted Answer

Both. Chatbots for conversational surfaces, autonomous agents for multi-step workflows — qualifying leads, drafting documents, reconciling invoices, running diagnostics — with tool use, memory and guardrails.

Question 10

How do you handle compliance — SOC 2, HIPAA, GDPR?

Accepted Answer

We architect to your compliance posture: data residency controls, audit logs for every prompt and tool call, role-based access, BAA-ready deployments for HIPAA, and DPIA support for GDPR.

Question 11

What is RAG and do we need it?

Accepted Answer

Retrieval Augmented Generation grounds an LLM in your own documents instead of guessing from training data. You need it whenever answers must reflect your contracts, policies, product or customer history.

Question 12

Can the AI take actions, not just answer?

Accepted Answer

Yes — through function calling and MCP we expose your APIs as tools. The agent can create tickets, update records, send emails, issue refunds, schedule meetings, with audit trails and optional human approval.

Question 13

How do you measure AI quality in production?

Accepted Answer

Offline evaluation suites (golden sets, LLM-as-judge, regression tests) plus online metrics — task success, deflection, CSAT, hallucination rate, cost per resolved interaction — surfaced in a live dashboard.

Question 14

Will AI replace our team?

Accepted Answer

No — it removes repetitive work so the team can focus on the 20% that needs judgement. Most clients reallocate capacity to higher-value work; headcount typically stays flat while throughput multiplies.

Question 15

Do you fine-tune models?

Accepted Answer

When prompting and RAG aren't enough — usually for tone, domain vocabulary or structured extraction. We fine-tune via LoRA / QLoRA on open-weight models, or via OpenAI / Anthropic managed fine-tuning.

Question 16

Who owns the IP?

Accepted Answer

You do. All prompts, evaluation sets, fine-tuned weights, vector indexes and source code are yours under our standard MSA.

Question 17

Can you work with our internal AI team?

Accepted Answer

Yes. We embed alongside in-house teams to accelerate delivery, do code reviews, run platform builds, or own discrete workstreams while your team owns the roadmap.

Question 18

What is MCP and why does it matter?

Accepted Answer

Model Context Protocol is an open standard for connecting AI models to tools and data sources. We build MCP servers around your internal systems so any compliant model can use them safely.

Question 19

How do you handle prompt injection and jailbreaks?

Accepted Answer

Input filtering, structured output enforcement, allow-listed tool calls, sandboxed execution, and continuous red-team testing. Production agents never run arbitrary code from user input.

Question 20

What happens after launch?

Accepted Answer

Continuous optimization — prompt updates, model upgrades, new tool integrations, evaluation expansion, and 24/7 monitoring. Our retainer clients get a dedicated AI ops engineer.

Automate business.
Accelerate growth.

A complete platform for applied AI

What changes when AI meets your industry

Compose autonomous business pipelines

Specialised agents for every function

Model-agnostic. Privacy-first. Production-grade.

Ten processes we automate end-to-end

The reference architecture we ship

The AI stack, end-to-end

Plugs into the systems you already run

Enterprise-grade by default

Numbers from real deployments

Shipped. Measured. Repeatable.

From 6-hour responses to 11 seconds

180k contracts reviewed in 14 days

5× lead-to-meeting conversion

See what AI automation is worth to you

Twelve reasons enterprises choose us

Twenty questions enterprises ask us

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