Why Bvyte

The case for hiring us, in writing.

The gap we close

Most AI projects stall somewhere between the demo and the P&L. We exist for the part in between: evaluation, integration, security review, change management, and the second month in production, when nobody is watching and everything still has to work.

Six reasons

What you are actually paying for.

Every AI vendor promises speed and quality. These are the six things we do differently, and each one shows up in the contract, not just the pitch.

01
The people in the pitch write the code.

Senior engineers only. No partner who sells and disappears, no junior bench learning on your budget. The team you meet is the team named in the statement of work.

Named team in every SOW
02
Priced to an outcome, not an hour.

Every phase is fixed fee, tied to a metric you choose, with an exit gate at the end. If the numbers do not justify the next phase, you stop and keep everything built so far.

Fixed fee · metric · exit gate
03
Evals before demos.

We build the evaluation harness in week one, so every claim about accuracy, cost and latency is measured on your data, against your baseline. Not on a cherry-picked screenshot.

Eval suite shipped with every system
04
Production is the deliverable.

Monitoring, runbooks, rollback, access control and cost guards ship with version one. We stay on to run it, or hand it to your team through a documented, rehearsed handover.

Runbooks, dashboards, on-call plan
05
You own all of it.

Code, prompts, fine-tuned weights, eval sets and documentation live in your repositories and your cloud from day one. No platform fee, no proprietary runtime, no lock-in.

IP assigned on payment, in your repo
06
A team that works while you sleep.

From Ahmedabad, US teams wake up to overnight progress and UK and EU teams share most of a working day. Every engagement guarantees four hours of live overlap, whatever your time zone.

IST base · 4 h guaranteed overlap

Credentials

The record, and where to check it.

Claims are cheap. Each line below comes with the evidence we will put in front of your security, procurement and engineering teams.

$40M+

Operating cost removed for clients

120+

Models and agents running in production

91%

Of proofs of value that reached production

7 wks

Median time from kickoff to first production release

Security & compliance: SOC 2 Type II aligned controls · ISO 27001 aligned information security · GDPR and India DPDP Act 2023 ready data processing agreements · HIPAA-aligned delivery for health data; Security pack and completed SIG Lite / CAIQ questionnaires, under NDA
Delivery record: Systems live across lending, insurance, health, retail, logistics and B2B SaaS, in the US, UK, EU, Middle East and India; Reference calls with clients in your industry
Team: Senior only: median eleven years across software, ML and data engineering. Every engineer background checked and under NDA before day one; Named CVs attached to every statement of work
Platforms: AWS, Google Cloud, Azure, Snowflake and Databricks. OpenAI, Anthropic, Google and open-weight models, chosen per use case, never per partnership; Certified engineers on each major cloud
Retention: Average client relationship of 2.8 years. Most engagements start with a two-week proof of value; Case studies in full, under NDA
Commercial: Master services agreement plus fixed-fee statements of work. Invoicing in USD, GBP, EUR or INR; Standard MSA shared on request

How we compare

Four ways to get AI and software built. One of them ships.

Each option has its place. Here is an honest view of the trade-offs, so you can decide where a studio like ours earns its fee.

	Bvyte.	Large consultancy	Freelancers	Hiring in-house
Time to first production system	6 to 10 weeks	6 to 12 months	Fast to a demo, slow to production	3 to 6 months to hire, then build
Who does the work	The senior engineers you met	Partners sell, juniors deliver	One person, one skill set	Your team, once hired
How you pay	Fixed fee per phase, tied to a metric	Time and materials, open ended	Hourly	Salaries, recruiting and tooling
Evaluation & monitoring	Built in week one	Often a separate workstream	Rarely	Depends on who you hire
Strategy and engineering	Same people, same room	Separate teams, handoffs	Engineering only	Strategy stays with leadership
Who owns the IP	You, in your repo, from day one	Often shared or licensed	You, if the contract says so	You
After launch	We run it, or hand over with a rehearsal	A new statement of work	Availability not guaranteed	Your team carries on-call

Case studies

Three systems, told properly.

The problem, what we built, and what changed. Client names are withheld at their request; full versions, with the numbers audited, are available under NDA.

An underwriting copilot that reads every file so analysts do not have to.

The problem

Analysts spent most of each application reading bank statements, pay stubs and tax returns, then retyping figures into a decision model. Backlogs peaked at month end, and good applicants walked to faster lenders.

What we built

A document agent that classifies and extracts from forty-plus document types, reconciles income across sources, flags inconsistencies with citations back to the page, and drafts the credit memo for an analyst to approve. Low-confidence fields route to a human queue. We evaluated it on 6,000 labelled historical files before it touched a live decision.

62%less manual review time per application
5 hmedian time to decision, down from 2.3 days
99.2%field accuracy on the audited sample

They built the eval set before they built the agent. That is why our model risk team signed off in one round.

VP Credit Operations · consumer lender

A claims intelligence pipeline for 1.2 million pages a month.

The problem

Medical reports, invoices and correspondence arrived as scans and email attachments. Handlers triaged by hand, complex claims waited in the same queue as simple ones, and nobody could say where time was being lost.

What we built

A governed ingestion and extraction platform with layout-aware parsing, claim summaries that cite their sources, a severity triage model, and drift monitoring on every stage. Everything runs inside UK data residency with a full audit trail that the insurer’s regulator review accepted without changes.

4.1×faster first decision on complex claims
1.2Mpages processed every month
31%of handler time moved to complex cases

Bvyte gave us the boring things first: lineage, access control, monitoring. The clever part landed on top of something we trust.

Head of Claims Transformation · specialty insurer

A support agent with human review for every edge case.

The problem

Tickets arrived in German and English across three product lines. Response times were slipping as the customer base grew, and any automation had to satisfy a strict GDPR review and a support team that had been burned by chatbots before.

What we built

A retrieval-grounded agent over docs, past tickets and release notes that can check account status, issue refunds under a threshold, and escalate with a drafted reply. It ran two weeks in shadow mode while agents rated every draft, and went live only when agreement cleared the target the support lead set.

41%of tickets resolved end to end
90 sfirst response, down from six hours
4.6CSAT out of 5, unchanged after launch

Our agents trained it by grading its drafts. By launch day it felt like a colleague, not a project.

Director of Customer Support · B2B SaaS

All six case studies

Playbooks

Six ways to start. Fixed scope, fixed fee.

You should know what happens every week before you sign anything. Each playbook ends at an exit gate: a measured decision to continue, change course or stop.

2 to 4 weeksFixed fee

Proof of Value

Best for a use case with a clear owner and a metric, and a board asking whether AI is real for your business.

Exit gate

Go only if the system beats your baseline by the margin we agreed on day one. If it does not, you keep the code, the data and the findings.

Week 1
Frame. Agree the metric and baseline, secure data access, and draft the evaluation set with your domain experts.
Week 2
Build. The thinnest working system on your real data, inside your cloud.
Week 3
Measure. Run the eval suite, model cost and latency at production volume, and red-team the failure modes.
Week 4
Decide. Readout to your sponsor with a go or no-go and a costed, fixed-price production plan.

You walk away with

A working prototype in your environment
An evaluation harness and labelled test set
A cost and latency model at real volume
A production plan with a fixed quote

6 to 8 weeksFixed fee

Agent Launch

Best for support, operations or internal knowledge work where decisions repeat and mistakes are expensive.

Exit gate

No autonomous action until shadow-mode agreement with your team clears the target your process owner sets.

Weeks 1 to 2
Map. Walk the workflow with the people who do it. Define tools, permissions, guardrails and approval rules.
Weeks 3 to 5
Build. Agent, integrations, human review queue, audit trail, and a red-team pass on every tool it can call.
Weeks 6 to 7
Shadow. The agent drafts, your people decide. We measure agreement, cost and time saved on live work.
Week 8
Launch. Staged rollout behind a kill switch, with dashboards, alerts and a runbook your team has rehearsed.

You walk away with

An agent in production with human review
A regression suite that runs on every change
Full audit trail of every action taken
Runbooks, dashboards and an on-call plan

8 to 12 weeksFixed fee

Data Foundation

Best for teams whose AI plans keep stalling on data quality, access or governance.

Exit gate

A first model served from the new platform with reproducible builds, lineage and monitoring your team can operate.

Weeks 1 to 2
Audit. Sources, lineage, access, PII and quality. A ranked list of what blocks your roadmap.
Weeks 3 to 6
Build. Governed pipelines, a semantic layer, and feature and vector stores on your cloud of choice.
Weeks 7 to 9
Operate. CI/CD for models, prompts and eval data. Observability, drift detection and incident runbooks.
Weeks 10 to 12
Prove. Ship the first use case on the platform and train your team to ship the next one without us.

You walk away with

A governed, documented data platform
Model and prompt deployment you can reproduce
Security controls mapped to SOC 2 and ISO 27001
A trained internal team

4 to 6 weeksFixed fee

Automation Sprint

Best for operations teams living in email, PDFs, spreadsheets and supplier portals.

Exit gate

Hours returned, measured against the week-one time study, not estimated from a slide.

Week 1
Discover. Time study with your operators. Score the top ten automation candidates by hours, risk and effort.
Weeks 2 to 4
Automate. The top three, end to end, inside your CRM, ERP and inbox, with exception queues for the rest.
Weeks 5 to 6
Harden. Measure hours returned, fix the edge cases, and train the operators who will own it.

You walk away with

Three workflows automated in production
An exception queue your team controls
A scored backlog of the next seven
A before-and-after time study

8 to 16 weeksFixed fee per milestone

Product Build

Best for a new web or mobile product, a platform rebuild, or a legacy system that needs to move fast without breaking.

Exit gate

A working release in production every week. If a milestone slips, you see it in the Friday demo, not at the deadline.

Weeks 1 to 2
Shape. Product spec, architecture and a clickable prototype, drafted with AI and decided by people.
Weeks 3 to 12
Build. Weekly releases. Agents scaffold, test and migrate; senior engineers design and review every line.
Every commit
Prove. Automated tests, security scans and quality gates in CI, with coverage your team can trust.
Launch
Hand over. Production launch, observability, docs and a rehearsed handover to your team or to Run & Improve.

You walk away with

A product in production, not a prototype
A clean, tested codebase in your repository
CI/CD, monitoring and documentation
A roadmap for the next release

MonthlyRetainer

Run & Improve

Best for systems already in production that must stay accurate, affordable and safe as models and data change.

Exit gate

Thirty days’ notice, any time. Full handover with a rehearsed on-call transfer, no exit fees.

Always
Monitor. Drift, cost, latency and incidents, with alerts routed to a named engineer.
Every sprint
Improve. Retraining, prompt and model upgrades, each one gated by the regression suite.
Monthly
Report. A one-page scorecard for your executive sponsor: metric, spend, incidents, next moves.
On call
Respond. Defined service levels, with critical incidents acknowledged within one hour.

You walk away with

A system that gets cheaper and better over time
Model upgrades without regressions
Monthly scorecards for leadership
Named engineers who know your system

Commitments

Four promises we put in the contract.

i
A named team.

The engineers in your statement of work do the work. Any change needs your written approval.
ii
Metric, or it is on us.

If a proof of value misses the metric we agreed, we run the next iteration at no fee.
iii
Your IP from day one.

Everything we build lives in your repositories and is assigned to you on payment.
iv
Leave whenever you like.

Thirty days’ notice, a full handover, and no exit fees. We would rather earn the renewal.

Questions

What procurement will ask.

Answered up front, so the first call can be about your problem.

Where is the team, and how do time zones work?

We are headquartered in Ahmedabad, India, and remote-first. Every engagement guarantees at least four hours of live overlap with your team. US clients usually get a morning standup plus overnight progress; UK and EU clients share most of a working day.

Who owns the code, models and data?

You do. Work happens in your repositories and your cloud accounts from day one, and all intellectual property is assigned to you on payment. We do not resell or reuse client data, prompts or fine-tuned weights.

How do you handle our data and security review?

We work inside your environment with least-privilege access, sign your DPA, and respect your data residency. We will complete your security questionnaire and share our security pack under NDA before any data is shared.

What does an engagement cost?

Each phase is a fixed fee agreed before kickoff, sized by the playbook and your scope. After a thirty-minute call we send a written scope and price within three business days. No open-ended time and materials.

Can you work alongside our in-house team?

That is how most engagements run. We pair with your engineers, follow your conventions and review process, and plan the handover from the first week, so your team can own the system when we step back.

What if the idea does not work?

Then you find out in weeks, not quarters, and for a fixed fee. Every playbook ends at an exit gate with a measured go or no-go. A clear no, with the evidence behind it, is a result too.

Start a project

The case for hiring us, in writing.

What you are actually paying for.

The people in the pitch write the code.

Priced to an outcome, not an hour.

Evals before demos.

Production is the deliverable.

You own all of it.

A team that works while you sleep.

The record, and where to check it.

Four ways to get AI and software built. One of them ships.

Three systems, told properly.

An underwriting copilot that reads every file so analysts do not have to.

A claims intelligence pipeline for 1.2 million pages a month.

A support agent with human review for every edge case.

Six ways to start. Fixed scope, fixed fee.

Proof of Value

Agent Launch

Data Foundation

Automation Sprint

Product Build

Run & Improve

Four promises we put in the contract.

A named team.

Metric, or it is on us.

Your IP from day one.

Leave whenever you like.

What procurement will ask.

Every model, every release, measurable answers.