Parvessh Agarwal
Founder & CEO
An entrepreneur at heart who connects business analysis, strategy, clear objectives and ROI with practical technology solutions.
LinkedIn →Hire AI developers
with one contact for the AI hire, not two vendors.
PixelCrayons is your point of contact for hiring an AI developer: we scope the role and write the proposal. Our sister company ValueCoders finds and supplies the AI engineer, or a team, under that single proposal and account team, so you interview the actual builder and sign one contract. Want PixelCrayons to scope and deliver a defined AI feature project ourselves instead of an embedded hire? See our AI services.
Interview before signing · One contact throughout
Tell us about the role
Start Your Enquiry
Send a short brief. Once we have a complete brief, we confirm the date you will receive your itemised proposal.
What you get
PixelCrayons is your point of contact for hiring an AI developer; our sister company ValueCoders sources and delivers the engineer. You brief us once, interview the proposed engineer directly before anything is signed, and keep escalation, billing and status with one PixelCrayons account team. For a scoped AI project instead of an embedded hire, see our AI services.
- Vetted on shipped features, not demosEvery engineer proposed has taken an AI feature into production, vetted against ValueCoders' delivery standards.
- One point of contact for the whole relationshipPixelCrayons scopes the role and handles escalation, billing and status; a ValueCoders engineer builds the feature.
- A straight referral either directionYou hear early if the job needs another specialist or a scoped project from our AI team.
Selected work
Judge the work,
not the adjectives.
Outcomes tied to real engagements, not averages.
2004
Established
500+
Agencies served
4,500+
Projects delivered
340%
Revenue growth · 7 months
eCommerce
+127%
Organic traffic · 5 months
SaaS
85%
Faster delivery · backlog cleared
via agency partner
Where the work happens
What they cover
AI feature skills,
shipped, not demoed.
Engineers vetted on real production AI features, not a prototype chasing the newest model release, sourced from ValueCoders' bench and proposed against your specific gap.
AI feature engineering
- LLM API integration: OpenAI, Anthropic and comparable providers, wired into an existing product
- Chatbot and conversational-agent development for web, WhatsApp and social surfaces
- AI-assisted search, recommendations and personalisation built on your existing catalogue or content
- Structured-output handling: function calling, JSON schemas, contracts the rest of the app can trust
- Model selection matched to the task on capability, cost and latency, not defaulted to whichever is newest
The reliability layer
- Failure-mode handling: what the feature does when the model is wrong, slow or unavailable
- Human-in-the-loop checkpoints on anything irreversible, designed in rather than bolted on
- Grounding responses in your actual data instead of the model's unverified recall
- Cost and latency engineering sized to real usage, not a demo's traffic
- Monitoring and alerting so a silent failure gets caught the same day, not the same quarter
The commercial layer
- Working inside your existing product and stack, not a bolt-on AI microservice nobody else touches
- Routing to a specialist role, or to a scoped PixelCrayons AI project, when the job needs one
- Code review as a discipline, not a formality before merge
- Documentation that lets a non-AI-specialist engineer maintain the feature after handover
- Client-ready reporting on what shipped, what's automated, and what still needs a human
In practice
What an AI developer
does once the demo is over.
Practical notes for briefing, interviewing and working alongside the person who puts a model inside your product.
Most of the week is not prompting
The visible part of an AI feature is a prompt and a response. The work sits around it. A typical week goes on building an evaluation set from real user inputs, checking what changed when a provider updates a model, tightening the parsing that turns model output into something your app can store, and chasing the cases where the answer looked fine but was wrong. Expect pull requests that touch logging, retries and database code more often than prompt files. If every update you get is about a cleverer prompt, ask what is being measured, and against which examples.
Interview questions that separate builders from demoers
Ask the candidate to walk you through an AI feature they shipped and what broke after launch. Strong candidates talk about specific failure cases: a malformed response that crashed a parser, a model that started refusing a harmless request, a cost spike from a loop nobody capped. Ask how they knew the feature was getting better or worse between releases. A good answer names an evaluation set and a way of scoring it. A weak answer names a model. Then ask what they would refuse to automate in your product. Someone who can't name anything hasn't thought about your users yet.
Have your data and your limits ready
Before the first day, gather the material the feature will be grounded in: the help articles, product records or documents it should answer from, with a note on which ones are out of date. Collect a set of real inputs users will send, including the awkward ones. Decide who holds the provider account and billing, and set a spending ceiling before anyone starts testing. Agree which data may leave your systems at all, since that rules some providers in or out. Hand over these decisions on day one and the first week goes on building rather than waiting.
Why AI hires go wrong
The common failure is a brief that says "add AI" without saying what a correct answer looks like. The developer then optimises for what impresses in a review meeting, and the feature drifts away from what customers need. The second failure is treating the model as the only moving part. Your data changes, your users find new phrasings and providers retire model versions, so a feature with no monitoring degrades quietly. The third is giving the model authority it shouldn't have, such as sending emails or changing records without a human check. Write down what success looks like, what must never happen, and who reviews edge cases.
Who they work alongside
An AI developer rarely works alone for long. Your backend team owns the APIs and data the feature reads from, so agree early who changes a schema when the feature needs a new field. Designers decide how uncertainty shows up in the interface: a note on confidence, a prompt to double-check, a route to a human. Support staff are often the first to see bad answers, so give them a simple way to flag one with the input attached. Product owners set the line between what the feature decides and what it only suggests. Name one person on your side who signs off that line.
When this is the wrong hire
If the task is moving data between tools on a trigger, a workflow automation build is usually simpler to maintain than custom model code. If your content is scattered, contradictory or out of date, the first job is cleaning it up, and an AI developer will spend weeks doing that under a job title that doesn't match the work. If you need a model trained from scratch on specialist data, that is research work with a different skill set. And if nobody on your side can say what a good answer looks like, hold off until someone can. This hire works best when the problem is already defined.
How it works
Brief to embedded,
one contact throughout.
Start
Brief and a call
We sign an NDA and you talk PixelCrayons through the product, the AI feature and the gap.
Once the brief is complete
Proposal and interview
We confirm when your proposal arrives, and ValueCoders names the engineer whose delivery history fits. You interview them directly before anything is signed.
Your decision
You sign off
You confirm the scope, the payment and the access we need. Nothing starts before this.
Typically 2 to 3 business days later
The engineer starts
With repo and API access in place, they review the existing product and data and join your stand-ups.
Every month
Scale either way
Add a specialist once the feature's scope calls for one, or step down once it's shipped, coordinated through the same PixelCrayons account team throughout.
Requests, approvals and the weekly review for this engagement live in your Prism workspace. Each decision is recorded against the outcome it expected. See how Prism runs an engagement →
Get a Proposal
Meet the actual people before anything is signed
Why through us
One contact,
the right engineer behind it.
PixelCrayons defines the AI role and holds the client relationship. ValueCoders supplies the engineer. You deal with one contact and get the right builder, with no confusion over who answers for the feature.
Vetted on shipped features, not demos
Every engineer proposed has taken an AI feature into production before yours, vetted against ValueCoders' own delivery standards. A demo shows what the feature does when everything works; delivery history shows what it does the day the model doesn't.
One point of contact for the whole relationship
PixelCrayons scopes the AI role and writes the proposal; one PixelCrayons team then handles escalation, billing and status while a ValueCoders engineer builds the feature.
A straight referral either direction
You hear early if the job needs a different specialist role, or if it's actually a scoped project our own AI services team should deliver directly rather than an embedded hire. No generalist stretched thin pretending to cover ground they don't.
Named plainly, never hidden
ValueCoders is named as the team delivering your AI engineer, on this page and for as long as the engagement runs. Who writes the model integration code stays clear long after the proposal is signed.
Side by side
How you typically hire,
versus through us.
An in-house hire earns its cost once the AI feature becomes core, ongoing product work rather than a bounded build, and we'll say so plainly if that's where you are. Most feature work starts smaller than that, or fits our own scoped AI projects better than an embedded hire.
| Hiring it yourself | Through PixelCrayons | |
|---|---|---|
| Time to a working feature | A full recruiting cycle: sourcing, interviews, notice periods, then a prototype that still isn't ready for production | Eligible work can typically start two to three business days after scope, payment and required access are confirmed |
| Vetting | A demo that looks impressive in the interview. Reliability under real usage shows up after launch | Delivery history on real production AI features, reviewed by ValueCoders' own standards |
| Management overhead | Yours entirely: recruiting, then reviewing every feature's failure modes yourself | PixelCrayons' account team handles coordination; you review the work and set priorities |
| Vendor relationships | However many agencies or freelancers it takes to cover the gap | One proposal, one point of contact, whichever engineer is actually assigned |
| Risk when it doesn't work | A feature that impressed in the demo breaks silently in production, with no monitoring to catch it | Propose-again is built in; leaving takes a handover call with the failure-mode notes, not a negotiation |
Related reading
All articlesWho is accountable
The people accountable
for the work.
Saundaraya Gupta
Technical Manager
Brings technical delivery, client communication and project management together so work moves from brief to release smoothly.
LinkedIn →Every engagement
A named coordinator and escalation contact
Know who coordinates your engagement, who owns the work and how to escalate an issue.
All engagements. Roles may be combined and vary with your engagement.
Before you sign
Our commitments
Each one says where it applies and links to its full terms.
Time-zone overlap
At least four hours a day inside your core working hours. Two hours plus a daily written handover for the US West Coast.
Managed specialists only; not a staffing promise for project teams.
Full termsClear IP & Ownership Terms
Your materials remain yours. Bespoke deliverable rights, licences and handover are agreed before work starts.
All engagements.
Full termsClear Delivery & Escalation Ownership
Know who coordinates your engagement, who owns the work and how to escalate an issue.
All engagements. Roles may be combined and vary with your engagement.
Full termsWhy not just hire a freelancer?
A freelancer can be the right choice for a single, well-defined task. We are set up for work that needs an accountable team: a named coordinator, delivery reviewed by QA and a senior lead before release, and continuity arrangements written into the engagement. Confidentiality, ownership and escalation terms are agreed in writing before work starts.
How does time-zone overlap work?
Your engineers work at least four hours a day inside your core working hours. We set the window with you before the engagement starts and hold it for as long as the engagement runs. For the US West Coast the live overlap is two hours each morning, with a written handover at the end of every Indian day. We do not run night shifts: engineers who work nights leave quickly, and keeping the same people on your team matters more to us than a longer overlap.
Can we try you before we commit?
For eligible managed specialists, yes: up to 14 calendar days or 80 logged working hours, whichever comes first. Stop within it and eligible trial hours are free; continue and they're billed at the agreed rate. Unpaid trial work stays ours. Project work and other roles get a paid pilot with scope, acceptance criteria, duration and fee agreed first.
What if we want to stop?
Cancellation, notice and any renewal terms are written into your proposal or statement of work before you accept it. If an engagement ends early, we hand over completed, paid-for work and list any unfinished work separately. For an eligible specialist trial, you can stop within the trial window and pay nothing for eligible trial hours.
How we handle claims, credentials and your data: group credentials, dated ratings, how case figures are signed off, client permissions and data handling.
Builder or demoer
How to tell an AI developer who ships
from one who demos.
ValueCoders proposes engineers vetted on production AI features. Your interview is the second filter, and these five questions show which side of that line a candidate is on.
| Ask about | A builder answers with | A demoer answers with |
|---|---|---|
| An AI feature they shipped, and what broke after launch | Specific failure cases: a malformed response that crashed a parser, a model that started refusing a harmless request, a cost spike from a loop nobody capped. | The launch, and nothing after it. |
| How they knew the feature got better or worse between releases | An evaluation set built from real user inputs, and a way of scoring it. | The name of a model. |
| What they need from you before the first day | The documents the feature should answer from, real user inputs including the awkward ones, a spending ceiling, and a decision on which data may leave your systems. | Model access, and nothing about your data or your limits. |
| What the feature does when the model is wrong, slow or unavailable | Failure-mode handling, plus monitoring and alerting so a silent failure is caught the same day. | A cleverer prompt, with nothing said about what is measured. |
| What a correct answer looks like in your product | Questions back to you: what success looks like, what must never happen, and who reviews edge cases. | A start on a brief that says "add AI" and never defines a correct answer. |
Moving data between tools on a trigger is usually simpler as a workflow automation build, and a defined AI feature with an end date often fits our own AI services better than an embedded hire.
Questions
Frequently
asked.
ValueCoders, our sister company. The AI engineer you interview and work with sits on ValueCoders' bench, while PixelCrayons scopes the role, writes the one proposal and stays your contact throughout. We say so openly here and during delivery, and never present their engineers as ours.
It depends on the shape of the need. An embedded hire through ValueCoders fits when you want a person joining your team long-term. A defined AI feature project with a clear scope and end date often fits our own AI services (/ai) better. We'll say on the first call which shape fits.
No, though they're related. This page is the entry point for an embedded AI-feature hire: LLM integration, chatbots and AI-assisted search in your product. Workflow automation (n8n, Zapier, Make), prompt design and evaluation, and retrieval or knowledge-system architecture have their own hire pages, and we'll route you there once scoping makes the need clear.
One account team, for the whole relationship. The proposal names ValueCoders and its part in delivering your AI engineer, yet escalation, billing coordination and status updates all come through the PixelCrayons account team you signed with.
Say so, early. Propose-again is built into the model: ValueCoders proposes a replacement who inherits the architecture notes and failure-mode decisions the first engineer kept, so a switch costs days, not a restart.
One proposal,
the right engineer behind it.
Brief PixelCrayons on the product and the AI feature you have in mind. We scope the role and stay your point of contact throughout; our sister company ValueCoders delivers the engineer under a written proposal, and you interview the actual person before anything is signed. If a scoped PixelCrayons-delivered project fits better than an embedded hire, we'll say so on the first call.
Interview before signing · One account team
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