Custom AI Sales Agents
The useful version of an AI sales agent is narrow: it does one repetitive job inside your revenue workflow, with defined inputs, a reviewable output, and a metric it is accountable to. We build that version.
Key Facts
- Focus
- AI sales agents
- Category
- GTM Services
- Defined outputs
- 5 deliverables
- Regions served
- India · United States · United Kingdom · UAE · Singapore
- Last reviewed
- 2026-09-10
The Generic AI SDR Fails for a Structural Reason.
Off-the-shelf AI sales agents are trained on the average company's motion and given access to the average company's data. Yours is neither. The result is confident output that is subtly wrong about your product, your segments and your competitors, and because it is fluent, nobody catches it until a prospect does. The fix is not a better model; it is narrower scope, grounded context, and a human checkpoint where the cost of being wrong is high.
Scope creep is the failure mode. An agent asked to research, qualify, write, send and follow up will do all five at roughly sixty percent quality, which is worse than doing one at ninety-five.
Ungrounded agents hallucinate about your own product. Without retrieval over your docs, pricing and closed-won notes, the model fills gaps with plausible fiction.
Nothing is measured. Most deployments cannot answer whether the agent saved time, because no one recorded how long the task took before.
How We Scope, Ground and Ship an Agent That Earns Its Keep
Pick One Job With a Baseline
We find the highest-volume repetitive task in your revenue workflow and measure it first: minutes per account, error rate, current throughput. If we cannot baseline it, we do not automate it, because there will be no way to prove the agent helped.
Ground It In Your Own Corpus
The agent retrieves from your product documentation, pricing, battlecards, past proposals and closed-won call notes before it generates anything. Every claim it makes traces back to a source your team can click through to, which is what makes review fast enough to be worth doing.
Put the Human Checkpoint Where Risk Lives
Internal research and CRM enrichment run unattended. Anything a customer will read passes a human first, at the pattern level rather than one item at a time. We design the review interface as carefully as the agent, because a checkpoint nobody uses is not a checkpoint.
Instrument, Evaluate, and Keep It Honest
We build an evaluation set from real historical cases and re-run it whenever a prompt, model or data source changes. Quality regressions surface in a dashboard rather than in a customer conversation, and you get a monthly number for hours returned and error rate.
Deliverables
- One production agent scoped to a single job, with a documented baseline and target
- A retrieval layer grounded in your product docs, pricing and closed-won history
- A review interface with approve, edit and reject paths that your reps will actually use
- An evaluation suite that runs on every change, with regression alerting
- Monthly reporting on hours returned, error rate and downstream pipeline effect
Is This You?
Strong fit
- You have a task that at least three people do more than twenty times a week in roughly the same way.
- You have written material (docs, calls, proposals) that a retrieval layer can ground the agent in.
- You are willing to keep a human in the loop on anything customer-facing for the first quarter.
Not a fit yet
- You want to replace your SDR team next month. The honest answer is that this compounds over quarters, not weeks.
- The process you want automated changes every few weeks and has never been written down.
Tell Us the Task You Hate Most
Bring the most repetitive job in your revenue workflow. We will tell you on the call whether an agent is the right tool for it, or whether a twenty-line automation would do the same work with none of the risk.
Book a 30-Min Strategy CallSend a Request
We'll be in touch!
Expect a call within 1 business day.
Common Questions
Which models do you build on?
Whichever fits the job and your data-residency requirements. We build model-agnostic so you are not locked to one vendor's pricing or roadmap. Research and drafting tasks usually justify a frontier model; classification and extraction often run well on something much smaller and cheaper.
Where does our data go?
Into your infrastructure and your model provider account, under your agreements. We do not route your data through a Kirality-owned service, and nothing you give us is used to train any public model. For regulated buyers we can keep the whole pipeline inside your cloud region.
What does this cost to run each month?
Inference is usually the smaller line item. Most agents we ship run in the low hundreds of dollars a month at typical mid-market volumes. The costs worth planning for are the data sources the agent depends on and the review time in the first quarter before you trust it.
Who owns the agent when the engagement ends?
You do. Prompts, code, evaluation sets and documentation live in your repository from the first commit. We build so that a competent engineer on your side can take it over, and we run a handover specifically to prove that they can.
Related GTM Systems
AI SDR Tools Evaluation
What AI SDR tools actually do well, where they fail, how to run a real evaluation, and when building a narrow agent beats buying a broad one.
Lead Enrichment Automation
Multi-source enrichment waterfalls with coverage tracking, cost control and freshness SLAs, so every record your reps and agents touch is trustworthy.
GTM Systems for AI Startups
GTM for AI companies: proving value against sceptical buyers, usage-based pricing instrumentation, evaluation-led sales and cost-aware unit economics.