AI AGENTS · CUSTOM PLATFORMS

AI agents that pay for themselves.

Repetitive work in support, finance, sales and operations quietly costs companies hundreds of thousands of dollars a year, and millions at scale. Polaris Forge Labs builds production AI agents that take that work off your team, with approvals, audit logs and cost caps from day one. And when off-the-shelf SaaS no longer fits, we build the platform your business owns.

Fixed scope, fixed feeAgreed in writing before work starts
Senior team onlyThe people you meet do the work
You own the codeTransferred on final payment
Your cloud, your dataDeployed in your accounts
$40Mprofit improvement Klarna projected from one AI support assistantKLARNA · 2024 $260Ma year in efficiency gains from AI-assisted code upgradesAMAZON · 2024 360,000 hrsof legal review a year handled by software in secondsJPMORGAN CHASE $50Ma year in seller time freed for revenue workLUMEN TECHNOLOGIES PUBLIC CASE STUDIES, NOT POLARIS CLIENTS · SOURCES BELOW ↓
Where the money is

Repetitive work is the biggest cost nobody puts in the budget.

Every hour a skilled person spends re-keying invoices, answering the same ticket or researching a lead is an hour you pay full price for. Agents do that work for cents. Here is what it adds up to at a 200-person company.

Customer support~$240K/yr

12 agents at $40K loaded cost. AI resolves half of tickets end-to-end and drafts the rest. 12 × $40K × 50%

Invoice and document processing~$770K/yr

8,000 invoices a month at ~$10 each to process by hand, ~$2 with extraction, matching and exception review. 96K × ($10 − $2)

Contract and compliance review~$630K/yr

2,000 contracts a year, 3 hours each at $150/hr. AI extracts terms and flags risk, cutting review time by 70%. 6K hrs × $150 × 70%

Internal knowledge and ops questions~$480K/yr

200 staff losing 2 hours a week hunting for answers at $50/hr. An assistant over your docs and systems halves it. 200 × 2 × 48 × $50 × 50%

Sales research and lead routing~$140K/yr

4 sellers spending half their week on research and qualification at $70K. Plus the revenue from faster speed to lead. 4 × $70K × 50%

ILLUSTRATIVE ESTIMATES USING COMMON INDUSTRY COST BENCHMARKS. YOUR NUMBERS WILL DIFFER. WE BASELINE THEM WITH YOU BEFORE QUOTING.

Estimate your savingsROI CALCULATOR
ANNUAL SAVINGS—
HOURS FREED / YEAR—
3-YEAR VALUE—
PAYBACK—

Assumes 48 working weeks, a $30K fixed-fee build and $2K a month to run (models, hosting, monitoring). A rough guide, not a quote.

Get a real estimate for your workflow
What we build

Two practices. One senior team.

01AGENTS · WORKFLOWS
AI agents in production

Agents that do real work inside your existing systems, with approvals, audit logs, evaluations and cost controls from day one.

Customer supporttriage · drafting · escalation
Finance operationsextraction · matching · reconciliation
Sales and lead handlingenrichment · scoring · routing
Documents and knowledgereview · search · answers
Agents we build →
02PLATFORMS · INTERNAL TOOLS
Custom operating platforms

The software your business runs on, built around how you actually operate. Replace a patchwork of SaaS subscriptions and spreadsheets with one platform you own.

Logistics operationsquoting · dispatch · tracking
E-commerce back officeorders · suppliers · margin
Pricing and revenue enginesrules · accounting · reporting
Portals and admin consolesclients · partners · ops
Discuss a platform →
Agents we build

Built for the workflows with the most repetitive volume.

Each agent is scoped to one workflow, measured against a baseline, and connected to the tools your team already uses. If it does not move the number we agreed, we have not finished.

Customer support

Ticket volume grows faster than the team.

Classifies and routes tickets, drafts replies from your help centre and order data, resolves the routine ones and escalates the rest with full context attached.

SAVESHours per ticket, night and weekend shifts, and the next round of hiring.
helpdeskorder datahelp centre
MEASURED BYFirst response time · resolution rate · escalation accuracy

Finance operations

Skilled people spend their week re-keying documents.

Extracts invoices and receipts, matches them to purchase orders and payments, reconciles accounts and surfaces only the exceptions a person needs to review.

SAVESCost per invoice, late-payment penalties and days off the month-end close.
accounting systemERPbank feeds
MEASURED BYProcessing time per document · exception rate · close time

Sales and lead handling

Good leads go cold while they wait to be qualified.

Enriches inbound leads, scores them against your ideal customer profile, routes them to the right owner, drafts first outreach and keeps the CRM clean.

SAVESSeller research hours, and revenue lost to slow follow-up.
CRMinboxenrichment data
MEASURED BYSpeed to lead · routing accuracy · CRM completeness

Contract and document review

Every agreement gets read line by line, by expensive people.

Pulls key terms, dates and obligations from contracts, forms and filings, compares them to your standard positions and flags what needs a human eye.

SAVESLegal and ops review hours, and errors from missed clauses.
document storee-signaturetemplates
MEASURED BYReview time per document · extraction accuracy · issues caught

Internal knowledge assistant

The answer exists somewhere, but nobody can find it.

Answers staff questions from your docs, wikis, tickets and databases with citations, and takes simple actions like filing requests or pulling reports.

SAVESTime hunting for information, and interruptions to your senior people.
wikidrivechatdatabases
MEASURED BYAnswer accuracy · questions deflected · time to answer

Catalog and data operations

Product and customer data is incomplete, inconsistent and stale.

Cleans, enriches and standardises records at scale: product attributes, descriptions, categorisation, deduplication and quality checks before anything goes live.

SAVESManual data-entry headcount, and conversions lost to poor listings.
PIMstorefrontdata warehouse
MEASURED BYRecords processed per hour · data quality score · error rate
What "in production" meansEVERY AGENT, BY DEFAULT
01 · INPUTYour systemsHelpdesk, ERP, CRM, inbox
02 · AGENTScoped toolsLeast-privilege access only
03 · GUARDRAILSChecks and capsPolicy, PII, spend, rate limits
04 · REVIEWHuman approvalWhere the risk warrants it
05 · RECORDAudit and evalsEvery action logged and tested
Case studies

What AI agents are already worth to the companies running them.

Public, sourced examples of the same patterns we build. These companies are not Polaris clients. They show what the work is worth at scale, and the same economics apply to a 50-person team. Our own client case studies will appear here as engagements complete.

KlarnaCUSTOMER SUPPORT
$40Mestimated profit improvement in 2024 from an AI support assistant
THE WORK
Millions of customer service chats across 23 markets and 35+ languages.
WHAT THE AI DOES
Handled 2.3M conversations in its first month, two-thirds of all chats, doing the work of about 700 full-time agents. Resolution time fell from 11 minutes to under 2, repeat inquiries dropped 25%, and satisfaction held level with human agents.
PATTERN: SUPPORT AGENTSource ↗
AmazonENGINEERING OPS
$260Min annualized efficiency gains, and 4,500 developer-years saved
THE WORK
Upgrading thousands of production applications to a newer Java version: slow, essential maintenance that typically took 50 developer-days per app.
WHAT THE AI DOES
An AI coding agent cut each upgrade to a few hours. Over half of production Java systems were upgraded in under six months, and 79% of the generated changes shipped without edits.
PATTERN: ENGINEERING AGENTSource ↗
JPMorgan ChaseDOCUMENT REVIEW
360,000 hrsof lawyer and loan-officer review a year, done in seconds
THE WORK
Interpreting around 12,000 commercial credit agreements a year by hand.
WHAT THE AI DOES
Its COiN system extracts about 150 key attributes from each agreement in seconds, and reduced loan-servicing mistakes caused by human misreading.
PATTERN: CONTRACT REVIEWSource ↗
Lumen TechnologiesSALES
$50Ma year in seller time redirected to revenue work
THE WORK
Sellers spent about four hours researching each customer before outreach.
WHAT THE AI DOES
AI-assisted research and meeting prep brought that to around 15 minutes, giving each seller roughly four hours back every week.
PATTERN: SALES RESEARCH AGENTSource ↗
WalmartCATALOG DATA
100×less headcount needed to clean 850M+ product data points
THE WORK
Creating and fixing product data across one of the world's largest catalogs.
WHAT THE AI DOES
Language models created or improved more than 850 million catalog data points. Executives said doing it without AI would have taken about 100 times the headcount in the same time.
PATTERN: DATA OPERATIONS AGENTSource ↗
Octopus EnergyCUSTOMER EMAIL
80% vs 65%customer satisfaction for AI-drafted emails versus human-only replies
THE WORK
A large share of inbound customer email for a fast-growing energy supplier.
WHAT THE AI DOES
Within three months AI was handling about a third of customer emails, the equivalent work of around 250 people, while scoring higher on satisfaction than human-written replies.
PATTERN: SUPPORT AGENT + HUMAN REVIEWSource ↗
YOUR WORKFLOW, NEXT The same patterns, scoped to your team and budget.

You do not need Amazon's scale. Run the same pattern on a 10-person support or finance team and the savings are often six figures a year. We will tell you honestly if yours are not.

Get a free opportunity brief →

FIGURES ARE AS REPORTED BY EACH COMPANY OR THE LINKED PUBLICATION AT THE TIME. POLARIS FORGE LABS WAS NOT INVOLVED IN THESE PROJECTS. TRADEMARKS BELONG TO THEIR OWNERS.

Our work

Built, launched and growing.

Products we designed, built and grew ourselves, from first line of code to real users. Client work is anonymized.

CONTENT DISCOVERY PLATFORMwhat-to-binge.com ↗
What to Binge
50K+visits and impressions from Google search
PROBLEMViewers spend longer choosing what to watch than watching. Keyword search and genre filters miss what people actually mean.
WHAT WE BUILTDescribe a mood or plot in plain language and get ranked recommendations across films, series, anime and manhua, with LLM costs capped daily.
Built with FastAPI · vector search · LLM reranking · React
ONLINE PARTY GAMESplaydringg.com ↗
Dringg
0apps to download, accounts to create or rules to explain
PROBLEMParty games need a physical deck or an app everyone has to download, and someone has to explain the rules.
WHAT WE BUILTGames run in the browser. One shared screen hosts, players join from their phones by QR code. No app, no sign-up.
Built with Node.js · WebSockets · real-time rooms
How we engage

Scope, fee and success criteria in writing first.

You know exactly what you are buying, and what it should save you, before you commit to anything.

01 · NO COSTOpportunity brief

A one-page note on where AI and software could remove cost and friction in your operations, with a rough savings estimate.

02 · 30–45 MINDiscovery call

We walk through the real workflow and agree what "better" means in numbers.

03 · WITHIN A WEEKFixed-scope proposal

Deliverables, exclusions, timeline, fee and acceptance criteria, then a statement of work.

04 · TYPICALLY 4–12 WEEKSBuild and hand over

Weekly demos of working software, deployed in your environment and documented.

Discovery sprint2–3 WEEKS · FIXED FEE

Workflow audit, a working prototype of the riskiest part and a costed build plan with projected savings.

Build4–12 WEEKS · TYPICALLY $10K–$50K

Design, build and deploy one agent or platform to agreed acceptance criteria.

Run and supportMONTHLY · RETAINER

Monitoring, evals, fixes and small improvements. Cancel with 30 days' notice.

About

Product judgement and engineering in the same people.

Polaris Forge Labs LLC is a US-registered studio serving companies in the US, UK, Europe, the Middle East, Australia and Asia. We are deliberately small: senior people who have led platform products at enterprise scale and still write the code.

Piyush KumarPK
Piyush Kumar Founder · Platform Products Leader
  • 7.5+ years leading platform, data and pricing products in payments, streaming, EV and marketplaces.
  • Previously at SonyLIV, Ola Electric, Zomato (Hyperpure) and OYO; products he led delivered over $1.8M in annual cost savings.
  • Ships AI products end to end: What to Binge and Dringg were designed, built and launched in-house.
  • IIT Kanpur.
How much can an AI agent actually save us?

It depends on volume and cost per task. Use the calculator above for a rough figure; in the discovery call we baseline your real numbers and only propose a build if the payback is clear.

Where does our data go?

We deploy into your cloud accounts. Model providers and data-retention settings are agreed in the statement of work.

Who owns what we build?

You do, on final payment. We keep only our pre-existing tools, which you get a licence to use.

What happens if a model gets something wrong?

Risky actions go to a person for approval, every action is logged, and we agree accuracy thresholds before launch.

What if scope changes?

We price the change in writing within three business days. Nothing changes until you sign it.

Which time zones do you cover?

We work from India Standard Time (UTC+5:30): full overlap with Asia-Pacific and the Gulf, mornings for the UK and Europe, and early-morning or late-evening calls for the US, with async updates in between.

Contact

Bring one workflow. We'll tell you if it's worth building.

A 30-minute call. You leave with an honest view of feasibility, rough cost, expected savings and the metric we would move.

New projects · hello@polarisforgelabs.com Existing clients · support@polarisforgelabs.com